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title: "List of examples of Stigler's law"
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source: "https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law"
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date_saved: "2026-05-05T03:17:59.384401+00:00"
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Stigler's law concerns the supposed tendency of eponymous expressions for scientific discoveries to honor people other than their respective originators.
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Examples include:
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== A ==
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Aharonov–Bohm effect. Werner Ehrenberg and Raymond E. Siday first predicted the effect in 1949, and similar effects were later rediscovered by Yakir Aharonov and David Bohm in 1959.
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Ampère's circuital law was inspired by the experimental results of André-Marie Ampère, and named in his honor. However, it was James Clerk Maxwell who combined those results into a single mathematical law.
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Arabic numerals, first developed in India around 7th century.
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Archimedes' screw is known to predate Archimedes by at least three centuries in ancient Babylon.
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Argand diagram by Caspar Wessel in 1797, predating Jean-Robert Argand by nine years.
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Arrhenius equation. The equation was first proposed by the Dutch chemist J. H. van 't Hoff in 1884; five years later in 1889, the Swedish chemist Svante Arrhenius provided a physical justification and interpretation for it.
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Auger effect. First discovered by Lise Meitner in 1922 and then, independently, in 1923 by Pierre Victor Auger.
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== B ==
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Bailey–Borwein–Plouffe formula was discovered by Simon Plouffe, who has since expressed regret at having to share credit for his discovery.
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Bell numbers have been studied since the 19th century and even medieval Japan, but are named after Eric Temple Bell who wrote about them in the 1930s.
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Bellman–Ford algorithm for computing the shortest-length path, proposed by Alfonso Shimbel, who presented the algorithm in 1954, but named after Richard Bellman and Lester Ford Jr., who published equivalent forms in 1956 and 1958.
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Benford's law, named after physicist Frank Benford, who stated it in 1938, although it had been previously stated by Simon Newcomb in 1881.
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Bertrand's ballot theorem proved using André's reflection method, which states the probability that the winning candidate in an election stays in the lead throughout the count. It was first published by W. A. Whitworth in 1878, nine years before Joseph Louis François Bertrand; Désiré André's proof did not use reflection, though reflection is now the method commonly taught.
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The Bessemer process was discovered by William Kelly in 1851. Henry Bessemer was the first to obtain a patent in 1855.
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The Bethe–Salpeter equation (named after Hans Bethe and Edwin Salpeter), which describes the bound states of a two-body system in quantum field theoretical. The equation was first published by Yoichiro Nambu, but without derivation.
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Betteridge's law of headlines, stating that when a headline asks a (yes-no) question, the answer is no. Considered "an old truism among journalists", it was well known before Betteridge wrote about it in 2009.
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Betz' law, which shows the maximum attainable energy efficiency of a wind turbine, was discovered first by Frederick W. Lanchester. It was subsequently independently rediscovered by Albert Betz and also Nikolai Zhukovsky.
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The Bilinski dodecahedron appears in a 1752 book by John Lodge Cowley but is named after Stanko Bilinski, who rediscovered it in 1960.
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The Black–Scholes model postulating a geometric Brownian motion as a model for stock market returns, credited to the 1973 academic papers of Fischer Black, Myron Scholes and Robert C. Merton, was first proposed by Paul Samuelson in 1965, and refined further in work with Merton in 1969.
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Blount's disease was described independently by C. Mau (1923) and Harald Nilsonne (1929), both writing in German, before it was described in English by Walter Putnam Blount (1937).
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Bode's law of 1772, stating that the distances of the planets from the sun follow a simple arithmetical rule, was first stated by Johann Titius in 1766, not Johann Elert Bode.
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The Bonferroni correction is named after Italian mathematician Carlo Emilio Bonferroni for its use of Bonferroni inequalities. However, its development is often credited to Olive Jean Dunn, who described the procedure's application to confidence intervals.
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Boyce–Codd normal form, a normal form used in database normalization. The definition of what we now know as BCNF appeared in a paper by Ian Heath in 1971. Date writes: Since that definition predated Boyce and Codd's own definition by some three years, it seems to me that BCNF ought by rights to be called Heath normal form. But it isn't.
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Boyle's law, which stipulates the reciprocal relation between the pressure and the volume of a gas, was first noted by Richard Towneley and Henry Power. In France, the law is known as Mariotte's law, after Edme Mariotte, who published his results later than Boyle, but crucially added that the relation holds only when temperature is kept constant.
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Bradley–Terry model, one of the most popular models for Pairwise comparison, first described by Ernst Zermelo in 1929.
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Braess's paradox, that adding one or more roads to a road network can cause overall traffic flow through it to slow down, was first discovered by Arthur Pigou in 1920.
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Brayton Cycle, as quoted from Wikipedia itself: The engine cycle is named after George Brayton (1830–1892), the American engineer who developed it originally for use in piston engines, although it was originally proposed and patented by Englishman John Barber in 1791.
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Brus equation named after Louis E. Brus. Proposed a few years earlier by Alexander Efros.
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Burnside's lemma, a counting technique in group theory, was discovered by Augustin Louis Cauchy, or possibly others. William Burnside originally attributed it to Ferdinand Georg Frobenius. Ironically, Burnside made many original contributions to group theory, and Burnside's Lemma is sometimes jokingly referred to as "the lemma that is not Burnside's".
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Buridan's ass originates from the Persian philosopher Al-Ghazali. The version popularised by Jean Buridan also does not include the eponymous donkey.
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source: "https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law"
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date_saved: "2026-05-05T03:17:59.384401+00:00"
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== C ==
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Cantor–Bernstein–Schröder theorem (also known by other variations, such as Schröder-Bernstein theorem) first proved by Richard Dedekind
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Cantor set, discovered in 1874 by Henry John Stephen Smith and introduced by German mathematician Georg Cantor 1883.
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Carmichael number: Václav Šimerka listed the first seven Carmichael numbers in 1885; they are named after Robert Daniel Carmichael who subsequently discovered the first one in 1910.
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Cartan matrices, first investigated by Wilhelm Killing.
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Cardano's formula, the solution to general cubic equations. Cardano stated that it was discovered by Scipione del Ferro, who passed the knowledge to his student Antonio Maria Fior. Around 1535 Niccolò Fontana Tartaglia learned of this from Fior and re-derived the formula for the cubic, which he later shared with Cardano.
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Cassegrain reflector, named after a design published in 1672 which has been attributed to Laurent Cassegrain, but was already known to Bonaventura Cavalieri in 1632 and Marin Mersenne in 1636.
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Cartesian duality: Named for René Descartes, but Teresa of Avila and her contemporaries wrote about similar methods of philosophical exploration eight to ten years before Descartes was born.
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Cavendish balance for measuring the universal gravitational constant, first devised and constructed by John Michell.
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The Cayley–Hamilton theorem was proven for the general case by Ferdinand Frobenius.
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Chandrasekhar limit, the mass upper limit of a white dwarf, was first derived by Wilhelm Anderson and E. C. Stoner, and later improved by Subrahmanyan Chandrasekhar.
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Chebyshev's inequality guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean. It was first formulated by his friend and colleague Irénée-Jules Bienaymé in 1853 and proved by Chebyshev in 1867.
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Chernoff bound, a bound on the tail distribution of sums of independent random variables, named for Herman Chernoff but due to Herman Rubin.
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Cobb–Douglas, a production function named after Paul H. Douglas and Charles W Cobb, developed earlier by Philip Wicksteed.
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Cooley–Tukey algorithm, named after J. W. Cooley and John Tukey, but invented 160 years earlier in 1805 by Carl Friedrich Gauss.
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Coriolis force which was previously recognized by others but was first mathematically described in an 1835 paper by Gaspard-Gustave de Coriolis
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Curie point, a critical temperature of phase change in ferromagnetism, named for Pierre Curie, who reported it in his thesis in 1895, but the phenomenon was found by Claude Pouillet before 1832.
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Currying, a technique for transforming an n-arity function to a chain of functions. Named after Haskell Curry who had attributed its earlier discovery to Moses Schönfinkel, though the principle can be traced back to work in 1893 by Gottlob Frege.
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== D ==
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Deming cycle of continuous improvement. Deming himself always referred to it as the "Shewhart cycle".
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De Morgan's laws of logic, transformation rules of propositional logic. Named after 19th-century British mathematician Augustus De Morgan, but already known to medieval philosophers such as Jean Buridan.
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Dunning-Kruger Effect was early warned by Bertrand Russell when he stated that "the fundamental problem with the world is that the stupid are cocksure while the intelligent are full of doubt".
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Dyson spheres are named after Freeman Dyson, but Dyson himself credited the original idea to Olaf Stapledon.
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== E ==
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Euler's number: the discovery of the constant itself is credited to Jacob Bernoulli, but it is named after Leonhard Euler.
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Euler's formula: an equivalent formula was proved by Roger Cotes 30 years before Euler published his proof.
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== F ==
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Fadeev–Popov ghosts, and their role in quantizing gauge theories, were first discovered by Richard Feynman. The second known work to make use of them was by Bryce DeWitt. Two weeks later, Ludwig Faddeev and Victor Popov published their work on the path integral treatment of these ghosts, leading Gerard 't Hooft and Martinus Veltman to choose their now standard name.
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Farey sequence. Cauchy published the proof to a conjecture put forth by Farey. Unknown to both men, similar results had been published earlier by Charles Haros.
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Fermi's golden rule, a quantum mechanical calculation, was discovered by Paul Dirac.
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The Fermi paradox, stated (in an unpublished work) by Konstantin Tsiolkovsky in 1933, long before Fermi. Tsiolkovsky, in turn, stated that others had already considered this question.
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The Floyd–Warshall algorithm for finding shortest paths in a weighted graph is named after Robert Floyd and Stephen Warshall who independently published papers about it in 1962. However, Bernard Roy had previously published an equivalent algorithm in 1959.
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The Fraunhofer lines in the solar spectrum were first noted by William Hyde Wollaston twelve years before they were rediscovered and studied systematically by Joseph von Fraunhofer.
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Fresnel lens. The idea of creating a thinner, lighter lens by making it with separate sections mounted in a frame is often attributed to Georges-Louis Leclerc.
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Frobenius elements in a Galois group of global fields were first created by Dedekind.
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Fibonacci numbers. Fibonacci was not the first to discover the famous sequence. They existed in Indian mathematics since 200 BC (Fibonacci gave the series in 1202 AD).
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source: "https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T03:17:59.384401+00:00"
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== G ==
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Galileo's paradox: the property of infinite sets was known to Duns Scotus.
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Gauss's law: first described by Joseph Louis Lagrange in 1773, over half a century before Gauss.
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Gauss's theorem: first proved by Ostrogradsky in 1831.
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Gaussian distribution: the normal distribution was introduced by Abraham de Moivre in 1733, but named after Carl Friedrich Gauss who began using it in 1794.
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Gaussian elimination: was already in well-known textbooks such as Thomas Simpson's when Gauss in 1809 remarked that he used "common elimination."
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Gibbs phenomenon: named for Josiah Willard Gibbs who published in 1901. First discovered by Henry Wilbraham in 1851.
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Goodhart's law, with several earlier variations, like Campbell's law.
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The Graetz circuit, also known as the diode bridge, was invented and patented in 1896 by Karol Pollak a year before it was published by Leo Graetz.
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The Graham escapement is often erroneously credited to English clockmaker George Graham but it was actually invented by astronomer Richard Towneley.
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The Gregorian telescope is named after James Gregory, who published it in 1663, but was already known to Bonaventura Cavalieri in 1632 and Marin Mersenne in 1636.
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Gresham's law was described by Nicolaus Copernicus in 1519, the year of Thomas Gresham's birth.
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Grimm's law, the first systemic sound change to be described, was first noted by Friedrich von Schlegel in 1806 and expanded by Rasmus Rask in 1818 before being extended by, and named after, Jacob Grimm in 1822.
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Gröbner basis: the theory was developed by Bruno Buchberger, who named them after his advisor, Wolfgang Gröbner.
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== H ==
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Halley's Comet was observed by astronomers since at least 240 BC, but named after Edmond Halley who computed its orbit and accurately predicted its return.
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Hasse diagrams were used by Henri Gustav Vogt three years before the birth of Helmut Hasse.
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Heaviside layer was named for Oliver Heaviside although work by Arthur E. Kennelly preceded Heaviside's proposal by several months.
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Hermite polynomials are named after Charles Hermite, though were studied earlier by Laplace and Chebyshev.
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Higgs field is named after Peter Higgs but was first theorized by Robert Brout and François Englert, albeit not published before Higgs had submitted his own paper.
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Heron's formula is named after Hero of Alexandria but is due to Archimedes.
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Hodrick–Prescott filter was popularized in the field of economics in the 1990s by economists Robert J. Hodrick and Nobel Memorial Prize winner Edward C. Prescott. However, it was first proposed much earlier by E. T. Whittaker in 1923.
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Hubble's law was derived by Georges Lemaître two years before Edwin Hubble.
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== I ==
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Ising model was invented by Wilhelm Lenz, but given to his student Ernst Ising to study.
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== J ==
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Jacobson's organ was first discovered by Frederik Ruysch before 1732.
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Jordan's Law (in the sense of sister species often being allopatric): Jordan himself gives Wagner credit for earlier observation of this pattern.
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== K ==
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Kapteyn's Star catalogued by Jacobus Kapteyn in 1898 was previously catalogued by B. A. Gould in 1873.
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Kasiski analysis: invented by Charles Babbage who recorded it in his diary but didn't otherwise publish it.
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Kepler's Supernova was first observed by Lodovico delle Colombe several days before Johannes Kepler
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Killing form: invented by Élie Cartan
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Kort nozzle was developed first by Luigi Stipa (1931) and later by Ludwig Kort (1934)
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Kuiper belt: theoretically described by a number of astronomers before Gerard Kuiper; Kuiper theorized that such a belt no longer existed.
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Kodály method was conceived and developed for music teaching by Jenő Ádám; a pupil of Kodály.
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Kolakoski sequence is named after William Kolakoski who described it in 1965, but Rufus Oldenburger previously discussed it in 1939.
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Kronecker product: Johann Georg Zehfuss already in 1858 described the matrix operation we now know as the Kronecker product
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== L ==
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L'Hôpital's rule to calculate the limit of quotient of functions at a point were both functions converge to 0 (or both converge to infinity) is named after Guillaume de l'Hôpital, but is generally believed to have been discovered by Johann Bernoulli.
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Lamarckism is generally used to refer to the idea of inheritance of acquired characteristics or soft inheritance, but the idea predates Jean-Baptiste Lamarck and was not the central part of his theory of transmutation of species.
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Laplace–Runge–Lenz vector was first discovered as a conserved quantity by Jakob Hermann and Johann Bernoulli.
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Leibniz formula for π was first discovered by 15th-century Indian mathematician Madhava of Sangamagrama, but it is named after Gottfried Leibniz after the latter discovered it independently 300 years later.
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Lexis diagram is named for Wilhelm Lexis but was previously theorized by Gustav Zeuner and Otto Brasche.
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Lhermitte's sign in neurology, the "barber chair phenomenon" was first described by Pierre Marie and Chatelin. French neurologist Jean Lhermitte published his first report three years later.
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The Liebig condenser, which Justus von Liebig popularized, was attributed to Göttling by Liebig himself, but had already been developed independently by Poisonnier, Weigel, and Gadolin.
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Liebig's law of the minimum was first developed by Carl Sprengel and only popularized by Justus von Liebig.
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Linus's law: named for Linus Torvalds, but actually described by Eric S. Raymond in The Cathedral and the Bazaar.
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source: "https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law"
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category: "reference"
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tags: "science, encyclopedia"
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instance: "kb-cron"
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== M ==
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Maxwell's equations. The modern form of the equations in their most common formulation is credited to Oliver Heaviside, based on James Clerk Maxwell's original work.
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Madelung rule, describing the order in which electron orbitals are filled, named after Erwin Madelung but first discovered by Charles Janet.
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Magellanic Clouds, while observed by Antonio Pigafetta on one of Magellan's voyages there were previous reports made by 16th century Italian authors Peter Martyr d'Anghiera and Andrea Corsali and earlier reports by Arabic astronomers.
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Matthew effect, named by Robert K. Merton after the writer of the Gospel of Matthew quoting the words of Jesus.
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Meadow's law, the formulation that one cot death in a family is tragic, two suspicious, and three murder, originally described by D.J. and V.J.M. Di Maio.
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Metropolis–Hastings algorithm. The algorithm was named after Nicholas Metropolis, who was the director of the Theoretical Division of Los Alamos National Laboratory at the time of writing the paper Equation of State Calculations by Fast Computing Machines. However, Metropolis did not contribute to that study in any way, as confirmed by various sources. The research problem was proposed by Augusta H. Teller and solved by Marshall N. Rosenbluth and Arianna W. Rosenbluth. Furthermore, according to Roy Glauber and Emilio Segrè, the original algorithm was invented by Enrico Fermi and reinvented by Stan Ulam.
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Moore's Law
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== N ==
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Newton's first and second laws of mechanics were known and proposed in separate ways by Galileo, Hooke and Huygens before Newton did in his Philosophiæ Naturalis Principia Mathematica. Newton owns the discovery of only the third one.
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Norman's law, proposed by Donald Norman, is a general restatement of Stigler's Law, "No saying or pronouncement is named after its originator." This law was named for Norman as an example of Stigler's Law – which was, itself, not named after its originator.
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Norton's theorem was published in November 1926 by Hans Ferdinand Mayer and independently discovered by Edward Lawry Norton who presented it in an internal Bell Labs technical report, dated November 1926.
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Nyquist–Shannon sampling theorem. The name Nyquist–Shannon sampling theorem honours Harry Nyquist and Claude Shannon, but the theorem was also previously discovered by E. T. Whittaker (published in 1915) and Shannon cited Whittaker's paper in his work. (from Wikipedia)
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== O ==
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The Oort cloud around the Solar System was first postulated by Ernst Öpik in 1932 and independently introduced by Jan Oort in 1960.
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Olbers' paradox was formulated by Kepler in the 17th century, long before Olbers was born.
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== P ==
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Padé approximant: named after and developed by Henri Padé around 1890, but was first introduced by Ferdinand Georg Frobenius.
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Pascal's triangle: studied by and named for Blaise Pascal, but constructed several times before him independently.
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Pearson's Coefficient of Correlation: was originally derived by Auguste Bravais and published in 1846.
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Pell's equation, studied in ancient India but mistakenly attributed to John Pell by Leonhard Euler. Apparently Euler confused Lord Brouncker (first European mathematician to find a general solution of the equation) with Pell.
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Penrose triangle, an impossible object first created by the Swedish artist Oscar Reutersvärd in 1934. The mathematician Roger Penrose independently devised and popularised it in the 1950s.
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Petersen graph as an example in graph theory, put forward by Julius Petersen in 1898, though it previously appeared in a paper by A. B. Kempe (1886).
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Pfizer vaccine, a COVID-19 mRNA vaccine developed by BioNTech. Due to its small size, BioNTech partnered with the pharmaceutical companies Pfizer and Fosun for support with clinical trials, logistics and manufacturing. The vaccine's clinical name is BNT162b2 and it is currently marketed under the name Comirnaty.
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Platonic solids were described earlier by Theaetetus, and some of them even earlier, by the Pythagoreans.
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Playfair's axiom, an alternative to Euclid's fifth postulate on parallel lines, first stated by Proclus in the 5th century AD but named after John Playfair after he included it in his 1795 book Elements of Geometry and credited it to William Ludlam.
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Playfair cipher, invented by Charles Wheatstone in 1854, but named after Lord Playfair who promoted its use.
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Poe's law, formally stated by Nathan Poe in 2005, but following Internet norms going back as far as Jerry Schwarz in 1983.
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The Poincaré disk model and the Poincaré half-plane model of hyperbolic geometry are named after Henri Poincaré who studied them in 1882. However, Eugenio Beltrami published a paper on these models previously in 1868.
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Poisson distribution: described by Siméon Denis Poisson in 1837, though the result had already been given in 1711-21 by Abraham de Moivre.
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Poisson spot: predicted by Fresnel's theory of diffraction, named after Poisson, who ridiculed the theory, especially its prediction of the existence of this spot. It is also called the Arago spot as François Arago observed it or the Fresnel bright spot after Augustin-Jean Fresnel's theory, though it had already been observed by Joseph-Nicolas Delisle and Giacomo F. Maraldi a century earlier.
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Prim's algorithm, developed in 1930 by the Czech mathematician Vojtěch Jarník and independently rediscovered by Prim in 1957.
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Prinzmetal angina, also known as variant angina, referring to angina (chest pain) caused by vasospasm of the coronary arteries. Described twice in the 1930s before being published by Prinzmetal in 1959.
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Pythagorean theorem, named after the mathematician Pythagoras, although it was known before him to Babylonian mathematicians (it is not known if the Babylonians possessed a proof of the result; nor is it known whether Pythagoras proved the result).
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source: "https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T03:17:59.384401+00:00"
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instance: "kb-cron"
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---
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== R ==
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The Reynolds number in fluid mechanics was introduced by George Stokes, but is named after Osborne Reynolds, who popularized its use.
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Richards equation is attributed to Richards in his 1931 publication, but was earlier introduced by Richardson in 1922 in his book "Weather prediction by numerical process." (Cambridge University press. p. 262) as pointed out by John Knight and Peter Raats in "The contributions of Lewis Fry Richardson to drainage theory, soil physics, and the soil-plant-atmosphere continuum" EGU General Assembly 2016.
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Russell's paradox is a paradox in set theory that Bertrand Russell discovered and published in 1901. However, Ernst Zermelo had independently discovered the paradox in 1899.
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== S ==
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The Sankey diagram was invented by Charles Joseph Minard
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The Schottky diode was neither discovered by Schottky nor its operation correctly explained by him. The actual nature of the metal–semiconductor junction was noted by Hans Bethe.
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The Schröder–Bernstein theorem in set theory was first stated without proof by Georg Cantor and first proved by Richard Dedekind
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Shuey's equation from 1985, which is an approximation of the Zoeprittz Equation first published in 1919.
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Simpson's paradox, a term introduced by Colin R. Blyth in 1972; but Edward Simpson did not actually discover this statistical paradox.
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The Simson line in geometry is named for Robert Simson, but cannot be found in Simson's works. Instead, it was first discovered by William Wallace in 1797.
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The Smith chart in radio frequency engineering is named after Phillip Hagar Smith, who published about it in 1939. However, it was independently invented by Tosaku Mizuhashi in 1937 and Amiel R. Volpert in 1939.
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Snell's law of refraction, named after Willebrord Snellius, a Dutch scientist, also known as Descartes law of refraction (after René Descartes) was discovered by Ibn Sahl.
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the Snellius–Pothenot problem was solved by Willebrord Snellius only, and restated by Laurent Pothenot 75 years later
|
||||
Steiner triple systems named for Jakob Steiner's work in 1754 were first found by Thomas Kirkman in 1746–1750.
|
||||
Stigler's law, attributed by Stephen Stigler himself to Robert K. Merton, though the phenomenon had previously been noted by others.
|
||||
Stirling's approximation, which was presaged in published work by Abraham de Moivre.
|
||||
Stokes's theorem discovered by Lord Kelvin
|
||||
Student's t-distribution, previously derived by Helmert and Lüroth.
|
||||
|
||||
== T ==
|
||||
The tetralogy of Fallot was described in 1672 by Niels Stensen, but named after Étienne-Louis Arthur Fallot who also described it in 1888.
|
||||
Taylor's law in ecology was discovered by H. Fairfield Smith in 1938 but named after L. R. Taylor who rediscovered it in 1961.
|
||||
Thévenin's theorem in circuit theory was discovered by Hermann von Helmholtz in 1853 but named after Léon Charles Thévenin who rediscovered it in 1883.
|
||||
Tai's model was known in antiquity.
|
||||
Tsiolkovsky rocket equation was independently arrived at by William Moore in 1810, Konstantin Tsiolkovsky in 1903, Robert Goddard in 1912, and Herman Oberth about 1920.
|
||||
|
||||
== V ==
|
||||
Venn diagrams are named after John Venn, who popularized them in the 1880s, but Leonhard Euler had already introduced them in 1768.
|
||||
Vigenère cipher was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. Sig. Giovan Battista Bellaso, but later misattributed to Blaise de Vigenère in the 19th century.
|
||||
The Von Neumann architecture of computer hardware is misattributed to John von Neumann because he wrote a preliminary report called "First Draft of a Report on the EDVAC" that did not include the names of the inventors: John Mauchly and J. Presper Eckert
|
||||
Voronoi diagrams are named after Georgy Voronoy, who defined and studied the general n-dimensional case in 1908, but have already been used by Descartes (1644), Lejeune Dirichlet (1850) and Snow (1854).
|
||||
|
||||
== W ==
|
||||
Wang tiles were hypothesized by Hao Wang not to exist, but an example was constructed by his student Robert Berger.
|
||||
Wheatstone bridge, an electrical measuring instrument invented by Samuel Hunter Christie in 1833, but named after Sir Charles Wheatstone who improved and popularized it in 1843.
|
||||
Widmanstätten patterns, named after Count Alois von Beckh Widmanstätten in 1808, but previously reported by William Thomson (mineralogist) in 1804.
|
||||
Wike's law of low odd primes, a principle of design of experiments, was stated by Sir Ronald A. Fisher in 1935 but named by Edwin Wike in 1973.
|
||||
Wilson Cycle, named in 1974 by Kevin C. A. Burke after the Canadian geologist J. Tuzo Wilson for Wilson's 1966 proposal that the Atlantic Ocean had previously closed and then opened again, a theory that the Swiss geologist Émile Argand had proposed in the 1920s.
|
||||
|
||||
== Y ==
|
||||
Yagi–Uda antenna, a successful and popular beam antenna, whose primary inventor was Shintaro Uda, but which was popularized by, and formerly popularly named for, his collaborator Hidetsugu Yagi.
|
||||
|
||||
== Z ==
|
||||
Zipf's law states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. The law is named after George Kingsley Zipf, an early twentieth century American linguist. Zipf popularized Zipf's law and sought to explain it, though he did not claim to have originated it. Jean-Baptiste Estoup was the first person to note this regularity in word frequencies.
|
||||
|
||||
== See also ==
|
||||
List of misnamed theorems
|
||||
List of multiple discoveries
|
||||
List of scientific priority disputes
|
||||
|
||||
== References ==
|
||||
49
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
Self-experimentation refers to single-subject research in which the experimenter conducts the experiment on themself. Usually, this means that a single person is the designer, operator, subject, analyst, and user or reporter of the experiment. Self-experimentation is an example of citizen science since it can be done by patients or people interested in their own health and well-being, as both research subjects and self-experimenters. It is also referred to as personal science or N-of-1 research.
|
||||
|
||||
|
||||
== Biology and medicine ==
|
||||
Human scientific self-experimentation principally (though not necessarily) falls into the fields of medicine and psychology. Self-experimentation has a long and well-documented history in medicine which continues to the present day.
|
||||
For example, after failed attempts to infect piglets in 1984, Barry Marshall drank a petri dish of Helicobacter pylori from a patient, and soon developed gastritis, achlorhydria, stomach discomfort, nausea, vomiting, and halitosis. The results were published in 1985 in the Medical Journal of Australia, and is among the most cited articles from the journal. He was awarded the Nobel Prize in Physiology or Medicine in 2005.
|
||||
Evaluations have been presented in the context of clinical trials and program evaluations.
|
||||
|
||||
|
||||
== Psychology ==
|
||||
In psychology, the best-known self-experiments are the memory studies of Hermann Ebbinghaus, which established many basic characteristics of human memory through tedious experiments involving nonsense syllables.
|
||||
|
||||
|
||||
== Chemistry ==
|
||||
Several popular and well-known sweeteners were discovered by deliberate or sometimes accidental tasting of reaction products. Saccharin was synthetized in 1879 in the chemistry labs of Ira Remsen at Johns Hopkins by a student scientist, Constantin Fahlberg, who noticed "curious sweet taste on his fingers while eating his dinner, [and] realized that it came from something he had spilled on his hand during the day". Fahlberg subsequently identified the active compound, ortho-benzoic sulfimide, and named it saccharin. Cyclamate was discovered when a chemistry research student noticed a sweet taste on his cigarette that he had set down on his bench. Acesulfame was discovered when a laboratory worker licked his finger. Aspartame was also discovered accidentally when chemist James Schlatter spilled a solution of it on his hands, then later licked one of his fingers to pick up a piece of paper. Sucralose was discovered by a foreign student, mishearing instructions of his supervisor, Prof. L. Hough, to "test" the compounds as to "taste" them.
|
||||
Leo Sternbach, the inventor of Librium and Valium, tested chemicals that he made on himself, saying in an interview, "I tried everything. Many drugs. Once, in the sixties, I was sent home for two days. It was an extremely potent drug, not a Benzedrine. I slept for a long time. My wife was very worried".
|
||||
Swiss chemist Albert Hofmann first discovered the psychedelic properties of LSD five years after its creation, when he accidentally absorbed a small amount of the drug through his fingertips. Days later, he intentionally self-experimented with it.
|
||||
Hungarian chemist and psychiatrist Stephen Szára discovered the psychedelic effects of dimethyltryptamine (DMT) via self-experimentation in 1956. He described experiencing intense euphoria at the higher DMT doses due to his excitement about the discovery.
|
||||
American chemist Alexander Shulgin synthesized hundreds of compounds in search of psychoactive drugs like psychedelics and entactogens, and evaluated them via careful self-experimentation together with his wife Ann Shulgin and a small research group of good friends.
|
||||
A great deal of additional notable self-experimentation in the area of psychoactive drugs has also been reported.
|
||||
|
||||
|
||||
== See also ==
|
||||
Psychonautics
|
||||
Participant observation
|
||||
Seth Roberts
|
||||
Personal science
|
||||
Human Enhancement
|
||||
Quantified self
|
||||
|
||||
|
||||
== Further reading ==
|
||||
Lawrence K. Altman: Who Goes First? The Story of Self-Experimentation in Medicine. (1987) Wellingborough
|
||||
Seth Roberts & Allen Neuringer: Self-Experimentation, In: Handbook of Research Methods in Human Operant Behavior von Kennon A. Lattal & M. Perone (Eds.), S. 619–655. New York: Plenum Press (englisch).
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
- Hanley et al 2019, "Review of Scientific Self-Experimentation: Ethics History, Regulation, Scenarios, and Views Among Ethics Committees and Prominent Scientists"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Self-experimentation refers to scientific experimentation in which the experimenter conducts the experiment on themself. Often this means that the designer, operator, subject, analyst, and user or reporter of the experiment are all the same. Self-experimentation has a long and well-documented history in medicine which continues to the present. Some of these experiments have been very valuable and shed new and often unexpected insights into different areas of medicine.
|
||||
There are many motivations for self-experiment. These include the wish to get results quickly and avoid the need for a formal organisational structure, to take the ethical stance of taking the same risk as volunteers, or just a desire to do good for humanity. Other ethical issues include whether a researcher should self-experiment because another volunteer would not get the same benefit as the researcher will get, and the question of whether informed consent of a volunteer can truly be given by those outside a research program.
|
||||
A number of distinguished scientists have undertaken self-experimentation, including at least five Nobel laureates; in several cases, the prize was awarded for findings the self-experimentation made possible. Many experiments were dangerous; various people exposed themselves to pathogenic, toxic or radioactive materials. Some self-experimenters, like Jesse Lazear and Daniel Alcides Carrión, died in the course of their research. Notable examples of self-researchers occur in many fields; infectious disease (Jesse Lazear: yellow fever, Max von Pettenkofer: cholera), vaccine research and development (Daniel Zagury: AIDS, Tim Friede: Snakebite), cancer (Nicholas Senn, Jean-Louis-Marc Alibert), blood (Karl Landsteiner, William J. Harrington), and pharmacology (Albert Hofmann, and many many others). Research has not been limited to disease and drugs. John Stapp tested the limits of human deceleration, Humphry Davy breathed nitrous oxide, and Nicholas Senn pumped hydrogen into his gastrointestinal tract to test the utility of the method for diagnosing perforations.
|
||||
|
||||
== Definition ==
|
||||
There is no formal definition of what constitutes self-experimentation. A strict definition might limit it to cases where there is a single-subject experiment and the experimenter performs the procedure on himself. A looser definition might include cases where the experimenters put themselves amongst the volunteers for the experiment. According to S. C. Gandevia of the University of New South Wales, who was looking at the question from the perspective of ethics, it is only self-experiment if the would-be self-experimenter would be named as an author on any subsequent published paper. That is, the person who would receive the academic credit for the experiment must also be the subject of it.
|
||||
|
||||
== Motivations ==
|
||||
|
||||
There are many reasons experimenters decide to self-test, but amongst the most fundamental is the ethical principle that the experimenter should not subject the participants in the experiment to any procedure they would not be willing to undertake themselves. This idea was first codified in the Nuremberg Code in 1947, which was a result of the trials of Nazi doctors at the Nuremberg trials accused of murdering and torturing victims in valueless experiments. Several of these doctors were hanged. Point five of the Nuremberg Code requires that no experiment should be conducted that is dangerous to the subjects unless the experimenters themselves also take part. The Nuremberg Code has influenced medical experiment codes of practice around the world, as has the exposure of experiments that have since failed to follow it such as the notorious Tuskegee syphilis experiment.
|
||||
Critics of self-experimenters point to other less savoury motivations such as simple self-aggrandisement. Some scientists have resorted to self-experiment to avoid the "red tape" of seeking permission from the relevant ethics committee of their institution. Werner Forssmann was so determined to proceed with his self-experiment that he continued with it even after permission had been denied. He was twice dismissed for this activity, but the importance of his work was eventually recognised in a Nobel Prize. Some researchers believe that self-experimentation is not permitted. However, this is not true, at least in the United States where the same rules apply regardless of who the subject of the experiment is.
|
||||
Self-experimentation is also criticised for the risk of over-enthusiastic researchers, eager to prove a point, not accurately noting the results. Against this it is argued by those supporting self-experiment that medically trained persons are in a better position to understand and record symptoms, and self-experiment is usually at the very early stage of a program before volunteers have been recruited.
|
||||
A wish to commit suicide is sometimes offered as a reason for self-experimentation. However, Lawrence K. Altman, author of Who Goes First?: The Story of Self-experimentation in Medicine, while acknowledging that this may sometimes occur, after extensive research could find only one verified case of attempted suicide by self-experimentation. This was Nobel Prize winner Élie Metchnikoff, who, in 1881, suffering from depression, injected himself with relapsing fever. This was his second suicide attempt, but according to his wife, Olga, he chose this method of death so that it would be of benefit to medicine. However, Metchnikoff survived and in 1892 also self-experimented with cholera, but this is not thought to have been a suicide attempt.
|
||||
|
||||
Perhaps the noblest motivation is the simple altruistic desire to do something of benefit to humanity regardless of the risks. There most certainly are risks, as Jesse Lazear found to his cost when he died of yellow fever after deliberately infecting himself. Max von Pettenkofer, after ingesting cholera bacteria said:Even if I had deceived myself and the experiment endangered my life, I would have looked Death quietly in the eye for mine would have been no foolish or cowardly suicide; I would have died in the service of science like a soldier on the field of honor.
|
||||
According to Ian Kerridge, professor of bioethics at the University of Sydney, the most common reason for undertaking self-experimentation is not so much anything noble, but rather "an insatiable scientific curiosity and a need to participate closely in their own research".
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
== Ethics ==
|
||||
As already mentioned, it is an ethical principle that the researcher should not inflict on volunteers anything that the researcher would not be willing to do to themself, but the researcher is not always a suitable, or even possible, subject for the experiment. For instance, the researcher may be the wrong sex if the research is into hormone treatment for women, or may be too old, or too young. The ethical question for the researchers is would they agree to the experiment if they were in the same position as the volunteers?
|
||||
Another issue that can lead researchers not to take part is whether the researcher would stand to gain any benefit from taking part in the experiment. It is an ethical principle that volunteers must stand to gain some benefit from the research, even if that is only a remote future possibility of treatment being found for a disease that they only have a small chance of contracting. Tests on experimental drugs are sometimes conducted on sufferers of an untreatable condition. If the researcher does not have that condition then there can be no possible benefit to them personally. For instance, Ronald C. Desrosiers in responding to why he did not test an AIDS vaccine he was developing on himself said that he was not at risk of AIDS so could not possibly benefit. Against that, the early stages of testing a new drug are usually focused merely on the safety of the substance, rather than any benefits it may have. Healthy individuals are required for this stage, not volunteers suffering from the target condition, so if the researcher is healthy, he or she is a potential candidate for testing. An issue peculiar to AIDS vaccine research is that the test will leave HIV antibodies in the volunteers blood, causing the person to show HIV positive when tested even if they have never been in contact with an HIV carrier. This could cause a number of social problems for the volunteers (including any self-testers) such as issues with life insurance.
|
||||
The ethics of informed consent is relevant to self-experimentation. Informed consent is the principle that the volunteers in the experiment should fully understand the procedure that is going to take place, be aware of all the risks involved, and give their consent to taking part in the experiment beforehand. The principle of informed consent was first enacted in the U.S. Army's research into Yellow fever in Cuba in 1901. However, there was no general or official guidance at this time. That remained the case until the yellow fever program was referenced in the drafting of the Nuremberg Code. This was further developed in the Declaration of Helsinki in 1964 by the World Medical Association which has since become the foundation for ethics committees' guidelines.
|
||||
|
||||
Some researchers believe that experimental research is too complex for the general public ever to be able to give proper informed consent. One such researcher is Eugene G. Laforet, who believes that the researchers taking part in the experiment themselves is more valuable to the volunteers than a legal consent form. Another is 1977 Nobel Prize winner Rosalyn S. Yalow who said "In our laboratory we always used ourselves because we are the only ones who can give truly informed consent." On the other side of the coin, there is the possibility that members of a research team may be coerced into participating by peer pressure.
|
||||
The question of who should be first to try the procedure in a new experiment is an ethical one. However, according to Altman it is not a question that can successfully be legislated. A law requiring self-test would force researchers to take risks that may sometimes be inappropriate. A code forbidding it might inhibit valuable discoveries.
|
||||
|
||||
Self-experimentation has a role in medical education. Although no longer encouraged, in former times it was perfectly standard to expect medical students to try for themselves the drugs they were going to be prescribing. Charles-Édouard Brown-Séquard, whose own self-experiments led him to the concept of what are now called hormones, was a nineteenth century proponent of the practice:I will suggest that you should study upon yourselves the effects of the most valuable remedies. I well believe that you will never know fully the action of certain remedies, if you have not ascertained, on your own person, what effects they produce on the brain, the eye, the ear, the nerves, the muscles, and the principal viscera.
|
||||
|
||||
== Value ==
|
||||
Self-experimentation has value in rapidly obtaining the first results. In some cases, such as with Forssmann's experiments done in defiance of official permission, results may be obtained that would never otherwise have come to light. However, self-experiment lacks the statistical validity of a larger experiment. It is not possible to generalise from an experiment on a single person. For instance, a single successful blood transfusion does not indicate, as we now know from the work of Karl Landsteiner, that all such transfusions between any two random people will also be successful. Likewise, a single failure does not absolutely prove that a procedure is worthless. Psychological issues such as confirmation bias and the placebo effect are unavoidable in a single-person self-experiment where it is not possible to put scientific controls in place.
|
||||
Such concerns do not apply so much if the self-experimenter is just one of many volunteers (as long as the self-experimenter is not also responsible for recording the results) but his or her presence still has value. As noted above, this can reassure the other participants. It also acts as a check on the experimenter when considering whether the experiment is ethical or dangerous.
|
||||
|
||||
== Notable examples ==
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Anaesthesia ===
|
||||
Dentist Horace Wells made multiple experiments with nitrous oxide, diethyl ether, and chloroform while trying to determine their uses as anaesthetics. The first, conducted in 1844, consisted of having his assistant John Riggs dose him with nitrous oxide and then extract one of his teeth. His later self-experimentation of ether and chloroform took place in 1848, and he eventually became addicted to chloroform due to excessive use. He inhaled chloroform as an anaesthetic shortly before committing suicide on January 24, 1848.
|
||||
Lidocaine, the first amino amide–type local anaesthetic, was first synthesized under the name xylocaine by Swedish chemist Nils Löfgren in 1943. His colleague Bengt Lundqvist performed the first injection anaesthesia experiments on himself.
|
||||
|
||||
=== Asthma ===
|
||||
Roger Altounyan developed the use of sodium cromoglycate as a remedy for asthma, based on khella, a traditional Middle Eastern remedy, with experiments on himself.
|
||||
|
||||
=== Blood ===
|
||||
|
||||
==== ABO blood group system ====
|
||||
Dr. Karl Landsteiner's discovery of the ABO blood group system in 1900 was based on an analysis of blood samples from six members of his laboratory staff, including himself.
|
||||
|
||||
==== Thrombocytopenia ====
|
||||
In the Harrington–Hollingsworth experiment in 1950, William J. Harrington performed an exchange blood transfusion between himself and a thrombocytopenic patient, discovering the immune basis of idiopathic thrombocytopenic purpura and providing evidence for the existence of autoimmunity.
|
||||
|
||||
=== Cancer ===
|
||||
In 1901, Nicholas Senn investigated whether cancer was contagious. He surgically inserted under his skin a piece of cancerous lymph node from a patient with cancer of the lip. After two weeks, the transplant started to fade and Senn concluded that cancer is not contagious.
|
||||
Much earlier, in 1808, Jean-Louis-Marc Alibert injected himself with a discharge from breast cancer. The site of injection became inflamed, but did not develop cancer.
|
||||
Gerhard Domagk, in 1949, injected himself with sterilised extract of human cancer in an attempt to prove that immunisation against cancer was possible.
|
||||
In 2024, Beata Halassy, a 50-year-old virologist with a history of recurrent triple-negative breast cancer (TNBC), conducted a self-experiment using intratumoral injections of research-grade viruses she prepared in her own laboratory. Her cancer was first treated with mastectomy and chemotherapy in 2016, but a local recurrence in 2018 left a small residual seroma. By 2020, what was thought of as a "seroma" had progressed into a 2 cm tumor infiltrating the pectoral muscle. Facing this invasive recurrence, she initially injected the Edmonston-Zagreb measles vaccine strain (MeV) directly into the tumor, later switching to the vesicular stomatitis virus Indiana strain (VSV). Her oncologists were informed of her intent and agreed to monitor her condition, prepared to intervene if necessary. This unconventional and experimental neoadjuvant oncolytic virotherapy led to significant tumor reduction, enabling a less invasive surgical resection. After that she completed a year of adjuvant trastuzumab therapy and remained recurrence-free 45 months after the oncolytic virotherapy.
|
||||
|
||||
=== Infectious diseases and vaccines ===
|
||||
COVID-19
|
||||
|
||||
In February 2020, Huang Jinhai, an immunologist at Tianjin University, claimed that he had taken four doses of a COVID-19 vaccine developed in his lab even before it had been tested in animals.
|
||||
In March 2020, the Rapid Deployment Vaccine Collaborative (also known as RaDVaC) developed, produced, and published technical specifications for a modular, intranasal COVID-19 vaccine. Numerous scientists working directly and indirectly on the group's vaccine development also began self-experimentation using the project's multiple vaccine candidates.
|
||||
In March 2020, Hans-Georg Rammensee, professor of immunology at University of Tübingen and co-founder of CureVac began testing a COVID-19 vaccine on himself.
|
||||
In May 2020, Alexander Gintsburg, director of the Gamaleya Research Institute of Epidemiology and Microbiology announced that several vaccine specialists had begun self-experimentation with the Sputnik V COVID-19 vaccine.
|
||||
|
||||
==== AIDS vaccine ====
|
||||
Daniel Zagury, in 1986, was the first to test his proposed AIDS vaccine.
|
||||
|
||||
==== Bartonellosis ====
|
||||
Daniel Alcides Carrión, in 1885, infected himself from the pus in the purple wart (verruga peruana) of a female patient. Carrión developed an acute form of bartonellosis now known as Carrion's disease or Oroya fever. This is a rare disease found only in Peru and certain other parts of South America. He kept detailed notes of his condition and succeeded in showing through this self-experiment that the chronic and acute forms were the same disease. He died from the disease after several weeks. A student who had assisted Carrion in carrying out this work was arrested and charged with murder, but later released.
|
||||
|
||||
==== Cholera ====
|
||||
|
||||
Max von Pettenkofer, in October 1892, drank bouillon deliberately infected with a large dose of cholera bacteria. Pettenkofer was attempting to disprove the theory of Robert Koch that the disease was caused by the bacteria Vibrio cholerae alone. Pettenkofer also took bicarbonate of soda to counter a claim by Koch that stomach acid killed the bacteria. Pettenkofer escaped with mild symptoms and claimed success, but the modern view is that he did indeed have cholera, luckily just a mild case, and possibly had some immunity from a previous episode.
|
||||
|
||||
==== Dysentery ====
|
||||
S.O. Levinson with H.J. Shaugnessy – and others between 1942 and 1947 – injected themselves with a vaccine against dysentery. The vaccine had previously been tested on mice, which had all died within minutes, and the effect on humans was completely unknown. The experimenters survived but suffered strong side effects.
|
||||
|
||||
==== Gastritis and peptic ulcers ====
|
||||
@ -0,0 +1,53 @@
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
===== Helicobacter pylori =====
|
||||
In 1984 a Western Australian scientist, Dr Barry Marshall, discovered the link between Helicobacter pylori (at that time known as Campylobacter pylori) and gastritis. This was based on a series of self-experiments that involved gastroscopy and biopsy, ingestion of H. pylori, regastroscopy and biopsy and subsequent treatment with tinidazole. His only option was self-experimentation: ethical measures forbade him from administering H. pylori to any other person. In 2005, Marshall and his long-time collaborator Robin Warren were awarded Nobel Prize in Physiology or Medicine, "for their discovery of the bacterium Helicobacter pylori and its role in gastritis and peptic ulcer disease".
|
||||
Marshall's experiment debunked the long-held belief of the medical profession that stress was the cause of gastritis. This cleared the way for the development of antibiotic treatments for gastritis and peptic ulcers and a new line of research into the likely role of H. pylori in stomach cancer.
|
||||
|
||||
===== Campylobacter jejuni =====
|
||||
Marshall's investigation was preceded by David A. Robinson who, in 1980, ingested Campylobacter jejuni, a bacterium found in cow's milk, to investigate whether gastritis could be caused by drinking milk infected with C. jejuni. Robinson became sick as a result. Robinson needed to do a human experiment because the alternative, testing on cows, was not viable as infected cows frequently do not become ill.
|
||||
|
||||
==== Malaria ====
|
||||
Tu YouYou and two colleagues tested qinghaosu on themselves before offering the treatment to patients.
|
||||
|
||||
==== Staphylococcus ====
|
||||
Gail Monroe Dack (1901–1976), a former president of the American Society for Microbiology, gave himself food poisoning by eating cake tainted with Staphylococcus.
|
||||
|
||||
==== Syphilis ====
|
||||
Constantin Levaditi (1874–1953) injected himself with spirochaete from rabbits suffering from syphilis but did not contract the disease himself.
|
||||
|
||||
==== Yellow fever ====
|
||||
|
||||
In Cuba, U.S. Army doctors from Walter Reed's research team infected themselves with yellow fever including
|
||||
James Carroll, Aristides Agramonte, and, most notably, Jesse Lazear, who died from yellow fever complications in 1900. These efforts ultimately resulted in proof of the mosquito-borne nature of yellow fever transmission and saved countless lives. Stubbins Ffirth had investigated the contagious nature of the disease at the end of the 18th century.
|
||||
There was an unsuccessful campaign to award a Nobel Prize to Reed's team. Lazear, in any event, could not be awarded the prize because it is never given posthumously. However, a Nobel Prize was awarded to a later yellow fever researcher and self-experimenter, Max Theiler who, in 1951, developed the first yellow fever vaccine and was the first to try it.
|
||||
|
||||
==== Trachoma ====
|
||||
Anatolii Al'bertovich Shatkin, in 1961, injected trachoma virus into the conjunctival sac of his eye and rapidly developed trachoma. He did not begin treatment of the condition for 26 days.
|
||||
|
||||
==== Schistosomiasis ====
|
||||
In July 1944, physician researcher Claude Barlow ingested over 200 schistosome worms to carry back to the United States from Egypt to study whether domestic snails could become infected and introduce the disease into the United States. Attempts to send infected snails, the intermediate host, by mail had been unsuccessful. He refused treatment, despite being desperately ill by December, so as not to lose the eggs for further study. He finally passed 4,630 eggs in his semen and 200 eggs in his urine. The U.S. government decided not to use the eggs, so his self-sacrifice was to no avail. It was November 1945 before he finally cleared all the parasites, after treatment with tartar emetic.
|
||||
|
||||
=== Non-infectious diseases ===
|
||||
|
||||
==== Anaemia ====
|
||||
William Bosworth Castle, in 1926, ate minced raw beef every morning, regurgitated it an hour later, and then fed it to his patients suffering from pernicious anaemia. Castle was testing his theory that there was an intrinsic factor produced in a normal stomach that hugely increased the uptake of the extrinsic factor (now identified as vitamin B12), lack of which leads to pernicious anaemia. Beef is a good source of B12, but patients did not respond with beef alone. Castle reasoned they lacked production of intrinsic factor and he could provide it from his own stomach. While Castle was not the recipient of this treatment, his story is included in Who Goes First?: The Story of Self-experimentation in Medicine and is considered a self-experimenter by the author. Victor Herbert made himself folate deficient to prove the deficiency causes pernicious anemia; the special diet also resulted in potassium and iron deficiencies.
|
||||
|
||||
==== Hyperthyroidism ====
|
||||
Elliott Cutler (1888–1947) took sufficient thyroid extract to give himself hyperthyroidism and enable him to study the effect of the condition on kidney function.
|
||||
|
||||
==== Scurvy ====
|
||||
In London in June 1769, William Stark aimed to find the cause of scurvy with a series of dietary experiments on himself. He devised a series of 24 dietary experiments and kept accurate measures of temperature and weather conditions, the weights of all food and water he consumed, and the weight of all daily excretions. He started with a basic diet of bread and water and became 'dull and listless'. When he recovered, he resumed experimenting by adding various foods, one at a time - olive oil, milk, roast goose, and others. After two months, he had symptoms of scurvy. By November 1769 he was living on nothing but honey puddings and Cheshire cheese. He considered testing fresh fruits and vegetables when he died in February 1770.
|
||||
|
||||
=== Drugs ===
|
||||
|
||||
==== Cocaine ====
|
||||
In 1936, Edwin Katskee took a very large dose of cocaine. He attempted to write notes on his office wall, but these became increasingly illegible as the experiment proceeded. Katskee was found dead the next morning.
|
||||
@ -0,0 +1,51 @@
|
||||
---
|
||||
title: "Self-experimentation in medicine"
|
||||
chunk: 5/7
|
||||
source: "https://en.wikipedia.org/wiki/Self-experimentation_in_medicine"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
==== Disulfiram ====
|
||||
In 1945, during the German occupation of Denmark, Erik Jacobsen and Jens Hald at the Danish drug company Medicinalco (which had a group of enthusiastic self-experimenters that called itself the "Death Battalion") were exploring the possible use of disulfiram to treat intestinal parasites, and in the course of testing it on themselves, accidentally discovered its effects (Disulfiram-alcohol reaction) when alcohol is ingested, which led several years later to the drug called Antabuse.
|
||||
|
||||
==== Furan ====
|
||||
Chauncey D. Leake, in 1930, took furan as a possible substitute for aspirin but it just gave him a splitting headache and painful urination that lasted three days.
|
||||
|
||||
==== Grapefruit juice ====
|
||||
David G. Bailey, in 1989, was researching the effects of drinking alcohol while taking the then experimental drug felodipine. It was usual in this kind of research to mix the alcohol with orange juice but Bailey did not like the taste of this drink so used grapefruit juice instead. Bailey found that there was three times more felodipine in his, and fellow researchers', blood than had been reported by other scientists using orange juice. It was later found that grapefruit juice suppresses an enzyme responsible for breaking down a large number of different drugs.
|
||||
|
||||
==== Ibuprofen ====
|
||||
As part of the team who developed ibuprofen in the 1960s, Stewart Adams initially tested it on a hangover.
|
||||
|
||||
==== Psychoactive drugs ====
|
||||
|
||||
Friedrich Sertürner isolated morphine from opium in 1804. Morphine was the first-ever alkaloid isolated from any plant. Sertürner wanted to prove his findings to his colleague with a public experiment on himself and three other friends.
|
||||
Jacques-Joseph Moreau published his study "Du Hachisch et de l'aliénation mentale" in 1845. He self-experimented with hashish and observed its varying effects on other people. Moreau insisted that researchers should self-experiment to gain understanding of the altered states of consciousness produced by psychoactive substances.
|
||||
Psychopharmacologist Arthur Heffter isolated mescaline from the peyote cactus in 1897 and conducted experiments on its effects by comparing the effects of peyote and mescaline on himself.
|
||||
Albert Hofmann discovered the psychedelic properties of LSD in 1943 by accidentally absorbing it and later intentionally ingesting it to verify that the effects were caused by LSD. He was also the first to isolate psilocybin from psilocybin mushrooms and self-experimented with it to prove it to be the active principle of psilocybin mushroom's psychoactive effects.
|
||||
Timothy Leary took LSD and was a well-known proponent of the social use of the drug in the 1960s.
|
||||
Alexander Shulgin synthesized and self-experimented with a variety of psychoactive drugs, notably MDMA. He developed a system known as the Shulgin Rating Scale for his research group to use during the self-experimentation of psychedelics.
|
||||
|
||||
=== Gases ===
|
||||
|
||||
==== Hydrogen ====
|
||||
|
||||
Around 1886, Nicholas Senn pumped nearly six litres of hydrogen through his anus. Senn used a rubber balloon holding four US gallons connected to a rubber tube inserted in the anus. An assistant sealed the tube by squeezing the anus against it. The hydrogen was inserted by squeezing the balloon while monitoring the pressure on a manometer. Senn had previously carried out this experiment on dogs to the point of rupturing the intestine. Senn was a pioneer of using this technique to determine if the bullet in gunshot wounds had penetrated the intestinal tract. In experiments on gunshot wounds to dogs, Senn verified that the gas escaping from the wound was hydrogen by setting light to it.
|
||||
Reports that Senn used helium in this experiment are almost certainly erroneous. Helium was first detected on Earth in 1882, but not isolated until 1895, and extractable reserves not found until 1903.
|
||||
|
||||
==== Synthetic gases ====
|
||||
Humphry Davy self-experimented with breathing of several different gases, most notably nitrous oxide.
|
||||
|
||||
=== Genes ===
|
||||
|
||||
Self-experimentation with gene therapies have been reported. Every gene therapy has a unique risk of harm, including the risk associated with the gene delivery method (i.e., the particular viral vector or form of transfection) that is used and the risk associated with a specific genetic modification. Examples of potential risks for some gene therapies include tissue damage and an immune response to foreign DNA, among many others.
|
||||
In 2017, biohacker Jo Zayner publicly injected herself with CRISPR, a gene-editing technology, during a biotechnology conference in San Francisco. Zayner targeted the myostatin gene, which encodes a protein that inhibits muscle growth, acting as a natural regulator to prevent excessive muscle development. By using CRISPR technology, she aimed to "knock out" this gene in the muscle cells of her forearm. The intent was to disrupt the gene's function, potentially allowing for localized muscle growth.
|
||||
|
||||
=== Pain ===
|
||||
Thomas Lewis and Jonas Kellgren studied pain in the 1930s. To do this, they injected hypertonic saline into various parts of their own bodies.
|
||||
In 1983, entomologist Justin O. Schmidt released a paper detailing what he called the Schmidt sting pain index based on his own personal reactions to the stings of various insects of the Hymenoptera order, rating them on a range from 0 to 4. His 1990 revised paper covered 78 such species.
|
||||
|
||||
=== Physical experiments ===
|
||||
@ -0,0 +1,53 @@
|
||||
---
|
||||
title: "Self-experimentation in medicine"
|
||||
chunk: 6/7
|
||||
source: "https://en.wikipedia.org/wiki/Self-experimentation_in_medicine"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
==== Hanging ====
|
||||
In the early 1900s Nicolae Minovici, a professor of forensic science in Bucharest, undertook a series of experiments into hanging. At first he put the noose around his neck while lying down and had an assistant put tension on the rope. He then moved on to full suspension by the neck. Finally, he attempted suspension with a slipping hangman's knot, but the pain was too great for him to continue. He could not swallow for a month. Minovici was determined to surpass a record set by Dr. Fleichmann of Erlangen, who in 1832, self-asphyxiated for two minutes. However, Minovici could not get close to this and disbelieved Fleichmann.
|
||||
Minovici and Fleichmann are not the only ones to self-experiment with strangulation. Graeme Hammond, a doctor in New York, tried it in 1882. Francis Bacon described an even earlier occasion in 1623 when the self-experimenter stepped off a stool with a rope around his neck, but was unable to regain his footing on the stool without assistance.
|
||||
|
||||
==== Rapid acceleration ====
|
||||
|
||||
John Paul Stapp, in 1954, sat in a rocket sled fired along rails in a series of steadily more violent tests. Speeds reached 631 mph, almost the speed of sound. This is a speed record for a manned rail vehicle that still stands today. At the end of the track the sled hit a trough of water that brought it to a rapid stop in around 1.4 seconds. In the most severe test, Stapp underwent an acceleration of 20 g as the rocket engine accelerated the vehicle up to speed and 46 g of deceleration (also a record) as the vehicle was brought to a stop. Stapp suffered numerous injuries in these tests (previous animal tests had shown that limbs could be broken merely by being pulled into the air stream), and several concussions. In the last test his eyes were bloodied as blood vessels burst in his eyes.
|
||||
These tests were carried out for the US Air Force to determine the forces that pilots could be subjected to and to enable better restraining straps to be designed.
|
||||
|
||||
==== Weight balance ====
|
||||
Santorio Santorio spent a large portion of 30 years living on a platform meticulously measuring his daily weight combined with that of his intake and excretion in an effort to test Galen's theory that respiration occurs through the skin as perspiratio insensibilis (insensible perspiration). The result was the 1614 publication De Statica Medicina ("On Medical Measurements").
|
||||
|
||||
=== Poisons ===
|
||||
|
||||
==== Black widow spider venom ====
|
||||
Allan Blair of the University of Alabama, in 1933, deliberately caused a black widow spider to bite him. At the time there was some doubt that the reported symptoms of some victims were the result of a spider bite or some other cause. Blair's experiment was intended to settle the matter. Blair became seriously ill and was hospitalised for several days in great pain, but survived.
|
||||
|
||||
==== Hydrogen cyanide ====
|
||||
|
||||
Joseph Barcroft, in 1917, tested hydrogen cyanide on himself as part of research into poison gas in World War I. He was shut in a chamber with a dog and exposed to the gas. Barcroft continued with the experiment even after the dog went into tetanic convulsions and appeared to die. The experiment was continued for less than two minutes. The next morning the dog was found to be alive and apparently fully recovered. It is not known why dogs are more susceptible to the gas than humans.
|
||||
|
||||
For other self-experiments by Barcroft, see § Temperature and pressure
|
||||
|
||||
==== Snake venom ====
|
||||
Tim Friede created his own vaccine against snakebite using pure venom injections from all four species of mambas, and four cobra species to achieve high immunity. He also survived anaphylactic shock six times during the development of his vaccine. Others have also injected venom to create immunity to snake venom: Bill Haast, Harold Mierkey, Ray Hunter, Joel La Rocque, Herschel Flowers, Martin Crimmins, and Charles Tanner.
|
||||
|
||||
==== Tetrachloroethylene and carbon tetrachloride ====
|
||||
In 1921, Maurice Crowther Hall ingested carbon tetrachloride to test its safety with a view to its possible use as a treatment for hookworm. Hall reported mild side effects. Carbon tetrachloride has since been found to cause acute liver failure. In 1925, Hall ingested tetrachloroethylene (the most common dry cleaning fluid) for the same purpose.
|
||||
|
||||
=== Radioactive materials and isotopes ===
|
||||
Gary Earl Leinbach, in 1972, swallowed radioactive iodine and a knife in a tube for a biopsy. Leinbach was investigating a new way of diagnosing steatorrhea.
|
||||
Kenneth Gordon Scott, in 1949, inhaled aerosols of plutonium and uranium.
|
||||
|
||||
==== Heavy water ====
|
||||
In 1935, pharmacologist Klaus Hansen drank heavy water to determine its effects on living beings. After his first dose yielded no ill effects, he began taking increasing doses on a daily basis. A follow-up report released a year later confirmed that he was in good health, and he lived to the age of 75.
|
||||
|
||||
=== Surgical and psychological procedures ===
|
||||
|
||||
==== Cardiac catheterization ====
|
||||
|
||||
Clinical application of cardiac catheterization began with Werner Forssmann in the 1930s, who inserted a catheter into the brachial vein of his own forearm, guided it fluoroscopically into his right atrium, and took an X-ray picture of it. Forssmann did this procedure without permission. He obtained the assistance of a nurse by deceiving her that she was to be the subject of the experiment. He tied down her arms while inserting the catheter into his own arm, only releasing her at the point it was too late to change, and he needed her assistance. Forssmann was twice fired for carrying out these self-experiments, but shared the Nobel Prize in Physiology or Medicine in 1956 for this achievement. Cardiac catheterization is now a routine procedure in heart surgery.
|
||||
|
||||
==== Self-surgery ====
|
||||
@ -0,0 +1,38 @@
|
||||
---
|
||||
title: "Self-experimentation in medicine"
|
||||
chunk: 7/7
|
||||
source: "https://en.wikipedia.org/wiki/Self-experimentation_in_medicine"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:50.473562+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
There have been several cases of surgeons operating on themselves, but most often it has been in the nature of an emergency rather than experiment. Such a case was Leonid Rogozov who was obliged to remove his own appendix in 1961 while stranded in Antarctica in winter. However, the first surgeon to carry out this self-operation, Evan O'Neill Kane in 1921, did so with an element of experiment. Although Kane's operation was necessary, it was not necessary to do it himself, so that in itself was experimental. More than that, Kane wished to experience the operation under local anaesthetic before trying the procedure on his patients. Kane advocated a reduction in the use of general anaesthetic by surgeons. In 2023, Michael Raduga, a Russian lucid dreaming researcher, performed self-neurosurgery that included trepanation, electrode implantation, and electrical stimulation of the motor cortex.
|
||||
|
||||
==== Sensory deprivation ====
|
||||
John C. Lilly developed the first sensory deprivation tanks and self-experimented them with the intention to study the origin of consciousness and its relation to the brain by creating an environment which isolates an individual from external stimulation.
|
||||
|
||||
==== Temperature and pressure ====
|
||||
Joseph Barcroft, in 1920, spent six days in a sealed glass chamber to investigate respiration at altitude. The partial pressure of oxygen was initially 163 mmHg falling to 84 mmHg (equivalent to an altitude of 18,000 ft) as the experiment progressed. Barcroft was attempting to disprove a theory of John Scott Haldane that the lungs actively secrete oxygen into the blood (rather than just through the process of passive diffusion) under conditions of low oxygen partial pressure. Barcroft suffered from severe hypoxia. At the end of experiment, part of Barcroft's left radial artery was removed for investigation.
|
||||
In 1931, Barcroft subjected himself to freezing temperatures while naked. Towards the end of the experiment he showed signs of the final stages of hypothermia. He was thought to be close to death and had to be rescued by colleagues.
|
||||
|
||||
==== Neural implant ====
|
||||
Kevin Warwick had an array of 100 electrodes fired into the median nerve fibres of his left arm. With this in place, over a 3-month period, he conducted a number of experiments linking his nervous system with the internet.
|
||||
|
||||
==== Neural adaption to immobilization ====
|
||||
Nico Dosenbach wore a pink cast over his (unbroken) right arm for two weeks in order to examine how brain circuits controlling movement are impacted by immobilizing illnesses or injuries. He did a 30-minute resting state fMRI study daily and identified an undiscovered pattern of pulses of rs-fMRI signal in motor regions controlling the disused anatomy. In a second experiment, he took psilocybin while in an fMRI scanner in a study he led as principal investigator.
|
||||
|
||||
== See also ==
|
||||
N of 1 trial
|
||||
Personal science
|
||||
Human Enhancement
|
||||
Quantified self
|
||||
|
||||
== References ==
|
||||
|
||||
== Further reading ==
|
||||
Haldane, J.B.S. (2001) [1927]. "On Being One's Own Rabbit". Possible Worlds (reprint ed.). Transaction Publishers. pp. 107–119. ISBN 978-0765807151.
|
||||
Roberts, Seth (December 2010). "The unreasonable effectiveness of my self-experimentation". Medical Hypotheses. 75 (6): 482–489. doi:10.1016/j.mehy.2010.04.030. PMC 2964443. PMID 20580874.
|
||||
Dagi, T. Forcht; Dagi, Linda Rabinowitz (1988). "Physicians Experimenting on Themselves: Some Ethical and Philosophical Considerations". In Spicker, S. F.; Alon, I.; de Vries, A.; Engelhardt, Jr, H. Tristram (eds.). The Use of Human Beings in Research: With Special Reference to Clinical Trials. Springer Science+Business Media. pp. 249–260. ISBN 9789400927056.
|
||||
Altman, Lawrence K. (1987). Who Goes First? The Story of Self-Experimentation in Medicine (1st ed.). New York: Random House. ISBN 978-0520212817.
|
||||
26
data/en.wikipedia.org/wiki/Serendipity-0.md
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26
data/en.wikipedia.org/wiki/Serendipity-0.md
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|
||||
---
|
||||
title: "Serendipity"
|
||||
chunk: 1/3
|
||||
source: "https://en.wikipedia.org/wiki/Serendipity"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:51.844802+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Serendipity is an unplanned fortunate discovery. The term was coined by Horace Walpole in 1754.
|
||||
The concept is often associated with scientific and technological breakthroughs, where accidental discoveries led to new insights or inventions. Many significant discoveries in history were serendipitous, including penicillin, Post-it notes, Popsicles, and the microwave oven, arising from unforeseen circumstances that were then recognized and capitalized upon.
|
||||
|
||||
== Definition ==
|
||||
Christian Busch views serendipity as "active luck", where chance encounters and human action come together. A missed flight or a casual walk in the park can lead to new friendships, interests, or even career opportunities.
|
||||
While serendipity in popular usage is often understood as a matter of pure chance, scientific discussions emphasize the crucial role of human agency—recognizing, interpreting, and acting upon unexpected opportunities. This interaction between chance and conscious action has been a key theme in areas such as creativity, leadership, innovation, and entrepreneurship.
|
||||
|
||||
== Etymology ==
|
||||
The first noted use of "serendipity" was by Horace Walpole on 28 January 1754. In a letter he wrote to his friend Horace Mann, Walpole explained an unexpected discovery he had made about a painting of Bianca Cappello, which he recently received from Mann as a gift. The finding regarded the coat of arms of the Cappello family and was categorised by reference to a Persian fairy tale, "The Three Princes of Serendip". The princes, he told his correspondent, were "always making discoveries, by accidents and sagacity, of things which they were not in quest of." The name comes from Serendip, an old Persian name for Sri Lanka (Ceylon), hence Sarandib by Arab traders. It is derived from the Sanskrit Siṃhaladvīpaḥ (Siṃhalaḥ, Sinhalese + dvīpaḥ, island), meaning Isle of the Sinhalas.
|
||||
The word has been exported into many other languages, with the general meaning of "unexpected discovery" or "fortunate chance".
|
||||
|
||||
== Applications ==
|
||||
|
||||
=== Inventions ===
|
||||
The term "serendipity" is often applied to inventions made by chance rather than intent. Andrew Smith, editor of The Oxford Companion to American Food and Drink, has speculated that most everyday products had serendipitous roots, with many early ones related to animals. The origin of cheese, for example, possibly originated in the nomad practice of storing milk in the stomach of a dead camel that was attached to the saddle of a live one, thereby mixing rennet from the stomach with the milk stored within.
|
||||
Other examples of serendipity in inventions include:
|
||||
33
data/en.wikipedia.org/wiki/Serendipity-1.md
Normal file
33
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@ -0,0 +1,33 @@
|
||||
---
|
||||
title: "Serendipity"
|
||||
chunk: 2/3
|
||||
source: "https://en.wikipedia.org/wiki/Serendipity"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:51.844802+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Carbonated water was invented by Joseph Priestley, independently and by accident, in 1767 when he discovered a method of infusing water with carbon dioxide after having suspended a bowl of water above a beer vat at a brewery in Leeds, Yorkshire. He wrote of the "peculiar satisfaction" he found in drinking it, and in 1772 he published a paper entitled Impregnating Water with Fixed Air.
|
||||
Vulcanization was discovered by Charles Goodyear in 1839 when he accidentally dropped rubber mixed with sulfur on a hot frying pan. He noticed that the resulting rubber was stronger and heat-resistant.
|
||||
Corn flakes were invented in 1894 when John Harvey Kellogg unintentionally left a batch of wheat-berry dough out over night. The next day, he decided to figure out what could be done to salvage it, rather than throwing it out. John, Will, and Ella Kellogg then discerned what happened and realized that this process could be reliably recreated through a process known as tempering.
|
||||
Safety glass first originated when French chemist Édouard Bénédictus accidentally dropped a glass flask in 1903 and noticed that it did not shatter like traditional glass. He then sought to refine the material to create a safer form of glass. He named his invention "triplex" since it consisted of two layers of glass separated by a thin layer of cellulose nitrate. Benedictus patented it in 1909, and triplex later became mass-produced.
|
||||
The Popsicle, whose origins go back to San Francisco where Frank Epperson, age 11, accidentally left a mix of water and soda powder outside to freeze overnight.
|
||||
The antibiotic penicillin, which was discovered by Sir Alexander Fleming after returning from a vacation to find that a Petri dish containing staphylococcus culture had been infected by a Penicillium mold, and no bacteria grew near it.
|
||||
The predecessor to ionization smoke detectors was created by Walter Jaeger in the late 1930s when he was trying to invent a poison gas sensor, which he failed to achieve. However, he noticed that the smoke from his cigarette caused the electric current in his circuit to drop, as shown on the meter. Subsequent modifications lead to the first commercial smoke detectors.
|
||||
The polymer teflon, which Roy J. Plunkett observed forming a white mass inside a pressure bottle during an effort to make a new CFCs refrigerant.
|
||||
In 1942, super glue was first created when a team of scientists headed by Harry Coover was trying to develop clear plastic gun sights for the war effort. They stumbled upon a formulation that stuck to everything with which it came in contact. The team quickly rejected the substance for the wartime application, but in 1951, while working as researchers for Eastman Kodak, Coover and a colleague, Fred Joyner, rediscovered cyanoacrylates, and then applied for a patent in 1954 which was issued in 1956.
|
||||
The effect on humans of the psychedelic lysergic acid diethylamide (LSD) was discovered by Swiss chemist Albert Hofmann in 1943, after unintentionally ingesting an unknown amount, possibly absorbing it through his skin.
|
||||
Silly Putty, which came from a failed attempt at synthetic rubber.
|
||||
The microwave oven. Raytheon scientist Percy Spencer first patented the idea behind it after noticing that emissions from radar equipment had melted the candy in his pocket.
|
||||
The Velcro hook-and-loop fastener. George de Mestral came up with the idea after a bird hunting trip when he viewed cockleburs stuck to his pants under a microscope and saw that each burr was covered with tiny hooks.
|
||||
The Post-It Note, which emerged after 3M scientist Spencer Silver produced a weak adhesive, and a colleague used it to keep bookmarks in place on a church hymnal.
|
||||
The use of sensors to prevent automobile air bags from killing children, which came from a chair developed by the MIT Media Lab for a Penn and Teller magic show.
|
||||
In 1989, the pharmaceutical company Pfizer was looking for a treatment for high blood pressure and angina. They accidentally discovered that their experimental drug, sildenafil citrate, had unexpected side effects of increasing blood flow to certain areas of the body. In recognition of this entirely new area of marketing potential, they decided to name their drug after the side effect, evoking the ideas of "vitality" and "Niagara", and called it "Viagra".
|
||||
|
||||
=== Discoveries ===
|
||||
|
||||
Serendipity contributed to entomologist Shaun Winterton discovering Semachrysa jade, a new species of lacewing, which he found not in its native Malaysia, but on the photo-sharing site Flickr. Winterton's discovery was aided by Flickr's ability to present images that are personalized to a user's interests, thereby increasing the odds he would chance upon the photo. Computer scientist Jaime Teevan has argued that serendipitous discovery is promoted by such personalisation, writing that "people don't know what to do with random new information. Instead, we want information that is at the fringe of what we already know, because that is when we have the cognitive structures to make sense of the new ideas."
|
||||
|
||||
=== Online activity ===
|
||||
Serendipity is a design principle for online activity that would present viewpoints that diverge from those participants already hold. Harvard Law professor Cass Sunstein argues that such an "architecture of serendipity" would promote a healthier democracy. Like a great city or university, "a well-functioning information market" provides exposure to new ideas, people, and ways of life. "Serendipity is crucial because it expands your horizons. You need that if you want to be free." The idea has potential application in the design of social media, information searches, and web browsing.
|
||||
43
data/en.wikipedia.org/wiki/Serendipity-2.md
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43
data/en.wikipedia.org/wiki/Serendipity-2.md
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@ -0,0 +1,43 @@
|
||||
---
|
||||
title: "Serendipity"
|
||||
chunk: 3/3
|
||||
source: "https://en.wikipedia.org/wiki/Serendipity"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:51.844802+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
== Related terms ==
|
||||
Several uncommonly used terms have been derived from the concept and name of serendipity.
|
||||
William Boyd coined the term zemblanity in the late twentieth century to mean somewhat the opposite of serendipity: "making unhappy, unlucky and expected discoveries occurring by design". The derivation is speculative, but believed to be from Nova Zembla, a barren archipelago once the site of Russian nuclear testing.
|
||||
Bahramdipity is derived directly from Bahram Gur as characterized in The Three Princes of Serendip. It describes the suppression of serendipitous discoveries or research results by powerful individuals.
|
||||
In addition, Solomon & Bronstein (2018) further distinguish between perceptual and realised pseudo-serendipity and nemorinity.
|
||||
|
||||
== See also ==
|
||||
Browse
|
||||
Coincidence
|
||||
Felix culpa
|
||||
Insight
|
||||
Lateral thinking
|
||||
Multiple discovery
|
||||
Role of chance in scientific discoveries
|
||||
Serendipaceratops
|
||||
Serendipity Sapphire
|
||||
Side effect
|
||||
Synchronicity
|
||||
|
||||
== References ==
|
||||
|
||||
== Further reading ==
|
||||
Merton, Robert K.; Barber, Elinor (2004). The Travels and Adventures of Serendipity: A Study in Sociological Semantics and the Sociology of Science. Princeton University Press. ISBN 978-0691117546. (Manuscript written 1958).
|
||||
Hannan, Patrick J. (2006). Serendipity, Luck and Wisdom in Research. iUniverse. ISBN 978-0595365517.
|
||||
Roberts, Royston M. (1989). Serendipity: Accidental Discoveries in Science. Wiley. ISBN 978-0471602033.
|
||||
Isabelle Rivoal and Noel B. Salazar (2013). Contemporary ethnographic practice and the value of serendipity, Social Anthropology, 21(2): 178–85.
|
||||
|
||||
== External links ==
|
||||
|
||||
ACM Paper on Creating serendipitous encounters in a geographically distributed community.
|
||||
The Serendipity Equations
|
||||
Serendipity of Science – a BBC Radio 4 series by Simon Singh
|
||||
Video: Are Scientific Discoveries Merely Lucky Shots?, Samantha Copeland, Delft University of Technology
|
||||
22
data/en.wikipedia.org/wiki/Solomon_four-group_design-0.md
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22
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@ -0,0 +1,22 @@
|
||||
---
|
||||
title: "Solomon four-group design"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Solomon_four-group_design"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:53.099465+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The Solomon four-group design is a research method developed by Richard Solomon in 1949. It is sometimes used in social science, psychology and medicine. It can be used if there are concerns that the treatment might be sensitized by the pre-test. In addition of the usual two groups (treatment and control), it has a second pair of groups who do not receive a pre-intervention evaluation.
|
||||
|
||||
|
||||
== Structure ==
|
||||
The structure of the trial is shown in the table :
|
||||
|
||||
The first two groups receive the evaluation test before and after the study, as in a normal two-group trial. The second groups receive the evaluation only after the study.
|
||||
The effectiveness of the treatment can be evaluated by comparisons between groups 1 and 3 and between groups 2 and 4.. In addition, the effect of the pre-treatment evaluation can be calculated by comparing the control group who received the pre-treatment evaluation with those who did not (groups 2 and 4).
|
||||
Various statistical treatments for the Solomon four-group design have been put forward, including Stouffer's Z and Monte Carlo.
|
||||
|
||||
|
||||
== References ==
|
||||
66
data/en.wikipedia.org/wiki/Source_criticism-0.md
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66
data/en.wikipedia.org/wiki/Source_criticism-0.md
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@ -0,0 +1,66 @@
|
||||
---
|
||||
title: "Source criticism"
|
||||
chunk: 1/5
|
||||
source: "https://en.wikipedia.org/wiki/Source_criticism"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:54.398591+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Source criticism (or information evaluation) is the process of evaluating an information source, i.e.: a document, a person, a speech, a fingerprint, a photo, an observation, or anything used in order to obtain knowledge. In relation to a given purpose, a given information source may be more or less valid, reliable or relevant. Broadly, "source criticism" is the interdisciplinary study of how information sources are evaluated for given tasks.
|
||||
|
||||
== Meaning ==
|
||||
Problems in translation: The Danish word kildekritik, like the Norwegian word kildekritikk and the Swedish word källkritik, derived from the German Quellenkritik and is closely associated with the German historian Leopold von Ranke (1795–1886). Historian Wolfgang Hardtwig wrote:
|
||||
|
||||
His [Ranke's] first work Geschichte der romanischen und germanischen Völker von 1494–1514 (History of the Latin and Teutonic Nations from 1494 to 1514) (1824) was a great success. It already showed some of the basic characteristics of his conception of Europe, and was of historiographical importance particularly because Ranke made an exemplary critical analysis of his sources in a separate volume, Zur Kritik neuerer Geschichtsschreiber (On the Critical Methods of Recent Historians). In this work he raised the method of textual criticism used in the late eighteenth century, particularly in classical philology to the standard method of scientific historical writing. (Hardtwig, 2001, p. 12739)
|
||||
|
||||
Historical theorist Chris Lorenz wrote:
|
||||
|
||||
The larger part of the nineteenth and twentieth centuries would be dominated by the research-oriented conception of historical method of the so-called Historical School in Germany, led by historians as Leopold Ranke and Berthold Niebuhr. Their conception of history, long been regarded as the beginning of modern, 'scientific' history, harked back to the 'narrow' conception of historical method, limiting the methodical character of history to source criticism. (Lorenz, 2001)
|
||||
|
||||
In the early 21st century, source criticism is a growing field in, among other fields, library and information science. In this context source criticism is studied from a broader perspective than just, for example, history, classical philology, or biblical studies (but there, too, it has more recently received new attention).
|
||||
|
||||
== Principles ==
|
||||
The following principles are from two Scandinavian textbooks on source criticism, written by the historians Olden-Jørgensen (1998) and Thurén (1997):
|
||||
|
||||
Human sources may be relics (e.g. a fingerprint) or narratives (e.g. a statement or a letter). Relics are more credible sources than narratives.
|
||||
A given source may be forged or corrupted; strong indications of the originality of the source increases its reliability.
|
||||
The closer a source is to the event which it purports to describe, the more one can trust it to give an accurate description of what really happened
|
||||
A primary source is more reliable than a secondary source, which in turn is more reliable than a tertiary source and so on.
|
||||
If a number of independent sources contain the same message, the credibility of the message is strongly increased.
|
||||
The tendency of a source is its motivation for providing some kind of bias. Tendencies should be minimized or supplemented with opposite motivations.
|
||||
If it can be demonstrated that the witness (or source) has no direct interest in creating bias, the credibility of the message is increased.
|
||||
Two other principles are:
|
||||
|
||||
Knowledge of source criticism cannot substitute for subject knowledge:
|
||||
|
||||
"Because each source teaches you more and more about your subject, you will be able to judge with ever-increasing precision the usefulness and value of any prospective source. In other words, the more you know about the subject, the more precisely you can identify what you must still find out". (Bazerman, 1995, p. 304).
|
||||
|
||||
The reliability of a given source is relative to the questions put to it.
|
||||
"The empirical case study showed that most people find it difficult to assess questions of cognitive authority and media credibility in a general sense, for example, by comparing the overall credibility of newspapers and the Internet. Thus these assessments tend to be situationally sensitive. Newspapers, television and the Internet were frequently used as sources of orienting information, but their credibility varied depending on the actual topic at hand" (Savolainen, 2007).
|
||||
|
||||
The following questions are often good ones to ask about any source according to the American Library Association (1994) and Engeldinger (1988):
|
||||
|
||||
How was the source located?
|
||||
What type of source is it?
|
||||
Who is the author and what are the qualifications of the author in regard to the topic that is discussed?
|
||||
When was the information published?
|
||||
In which country was it published?
|
||||
What is the reputation of the publisher?
|
||||
Does the source show a particular cultural or political bias?
|
||||
For literary sources complementing criteria are:
|
||||
|
||||
Does the source contain a bibliography?
|
||||
Has the material been reviewed by a group of peers, or has it been edited?
|
||||
How does the article/book compare with similar articles/books?
|
||||
|
||||
== Levels of generality ==
|
||||
Some principles of source criticism are universal, other principles are specific for certain kinds of information sources.
|
||||
There is today no consensus about the similarities and differences between source criticism in the natural science and humanities. Logical positivism claimed that all fields of knowledge were based on the same principles. Much of the criticism of logical positivism claimed that positivism is the basis of the sciences, whereas hermeneutics is the basis of the humanities. This was, for example, the position of Jürgen Habermas. A newer position, in accordance with, among others, Hans-Georg Gadamer and Thomas Kuhn, understands both science and humanities as determined by researchers' preunderstanding and paradigms. Hermeneutics is thus a universal theory. The difference is, however, that the sources of the humanities are themselves products of human interests and preunderstanding, whereas the sources of the natural sciences are not. Humanities are thus "doubly hermeneutic".
|
||||
Natural scientists, however, are also using human products (such as scientific papers) which are products of preunderstanding (and can lead to, for example, academic fraud).
|
||||
|
||||
== Contributing fields ==
|
||||
|
||||
=== Epistemology ===
|
||||
Epistemological theories are the basic theories about how knowledge is obtained and are thus the most general theories about how to evaluate information sources.
|
||||
52
data/en.wikipedia.org/wiki/Source_criticism-1.md
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52
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@ -0,0 +1,52 @@
|
||||
---
|
||||
title: "Source criticism"
|
||||
chunk: 2/5
|
||||
source: "https://en.wikipedia.org/wiki/Source_criticism"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:54.398591+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Empiricism evaluates sources by considering the observations (or sensations) on which they are based. Sources without basis in experience are not seen as valid.
|
||||
Rationalism provides low priority to sources based on observations. In order to be meaningful, observations must be explained by clear ideas or concepts. It is the logical structure and the well definedness that is in focus in evaluating information sources from the rationalist point of view.
|
||||
Historicism evaluates information sources on the basis of their reflection of their sociocultural context and their theoretical development.
|
||||
Pragmatism evaluate sources on the basis of how their values and usefulness to accomplish certain outcomes. Pragmatism is skeptical about claimed neutral information sources.
|
||||
The evaluation of knowledge or information sources cannot be more certain than is the construction of knowledge. If one accepts the principle of fallibilism then one also has to accept that source criticism can never 100% verify knowledge claims. As discussed in the next section, source criticism is intimately linked to scientific methods.
|
||||
The presence of fallacies of argument in sources is another kind of philosophical criterion for evaluating sources. Fallacies are presented by Walton (1998). Among the fallacies are the ad hominem fallacy (the use of personal attack to try to undermine or refute a person's argument) and the straw man fallacy (when one arguer misrepresents another's position to make it appear less plausible than it really is, in order more easily to criticize or refute it.)
|
||||
|
||||
=== Research methodology ===
|
||||
|
||||
Research methods are methods used to produce scholarly knowledge. The methods that are relevant for producing knowledge are also relevant for evaluating knowledge. An example of a book that turns methodology upside-down and uses it to evaluate produced knowledge is Katzer; Cook & Crouch (1998).
|
||||
|
||||
=== Science studies ===
|
||||
|
||||
Studies of quality evaluation processes such as peer review, book reviews and of the normative criteria used in evaluation of scientific and scholarly research. Another field is the study of scientific misconduct.
|
||||
Harris (1979) provides a case study of how a famous experiment in psychology, Little Albert, has been distorted throughout the history of psychology, starting with the author (Watson) himself, general textbook authors, behavior therapists, and a prominent learning theorist. Harris proposes possible causes for these distortions and analyzes the Albert study as an example of myth making in the history of psychology. Studies of this kind may be regarded a special kind of reception history (how Watson's paper was received). It may also be regarded as a kind of critical history (opposed to ceremonial history of psychology, cf. Harris, 1980). Such studies are important for source criticism in revealing the bias introduced by referring to classical studies.
|
||||
|
||||
=== Textual criticism ===
|
||||
|
||||
Textual criticism (or broader: text philology) is a part of philology, which is not just devoted to the study of texts, but also to edit and produce "scientific editions", "scholarly editions", "standard editions", "historical editions", "reliable editions", "reliable texts", "text editions" or "critical editions", which are editions in which careful scholarship has been employed to ensure that the information contained within is as close to the author's/composer's original intentions as possible (and which allows the user to compare and judge changes in editions published under influence by the author/composer). The relation between these kinds of works and the concept "source criticism" is evident in Danish, where they may be termed "kildekritisk udgave" (directly translated "source critical edition").
|
||||
In other words, it is assumed that most editions of a given works is filled with noise and errors provided by publishers, why it is important to produce "scholarly editions". The work provided by text philology is an important part of source criticism in the humanities.
|
||||
|
||||
=== Psychology ===
|
||||
|
||||
The study of eyewitness testimony is an important field of study used, among other purposes, to evaluate testimony in courts. The basics of eyewitness fallibility includes factors such as poor viewing conditions, brief exposure, and stress. More subtle factors, such as expectations, biases, and personal stereotypes can intervene to create erroneous reports. Loftus (1996) discuss all such factors and also shows that eyewitness memory is chronically inaccurate in surprising ways. An ingenious series of experiments reveals that memory can be radically altered by the way an eyewitness is questioned after the fact. New memories can be implanted and old ones unconsciously altered under interrogation.
|
||||
Anderson (1978) and Anderson & Pichert (1977) reported an elegant experiment demonstrating how change in perspective affected people's ability to recall information that was unrecallable from another perspective.
|
||||
In psychoanalysis the concept of defence mechanism is important and may be considered a contribution to the theory of source criticism because it explains psychological mechanisms, which distort the reliability of human information sources.
|
||||
|
||||
=== Library and information science (LIS) ===
|
||||
|
||||
In schools of library and information science (LIS), source criticism is taught as part of the growing field of information literacy.
|
||||
Issues such as relevance, quality indicators for documents, kinds of documents and their qualities (e.g. scholarly editions) are studied in LIS and are relevant for source criticism. Bibliometrics is often used to find the most influential journal, authors, countries and institutions. Librarians study book reviews and their function in evaluating books.
|
||||
In library and information science the checklist approach has often been used. A criticism of this approach is given by Meola (2004): "Chucking the checklist".
|
||||
Libraries sometimes provide advice on how their users may evaluate sources.
|
||||
The Library of Congress has a "Teaching with Primary Sources" (TPS) program.
|
||||
|
||||
=== Ethics ===
|
||||
|
||||
Source criticism is also about ethical behavior and culture. It is about a free press and an open society, including the protecting information sources from being persecuted (cf., Whistleblower).
|
||||
|
||||
== In specific domains ==
|
||||
|
||||
=== Photos ===
|
||||
43
data/en.wikipedia.org/wiki/Source_criticism-2.md
Normal file
43
data/en.wikipedia.org/wiki/Source_criticism-2.md
Normal file
@ -0,0 +1,43 @@
|
||||
---
|
||||
title: "Source criticism"
|
||||
chunk: 3/5
|
||||
source: "https://en.wikipedia.org/wiki/Source_criticism"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:54.398591+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Photos are often manipulated during wars and for political purposes. One well known example is Joseph Stalin's manipulation of a photograph from May 5, 1920, on which Stalin's predecessor Lenin held a speech for Soviet troops that Leon Trotsky attended. Stalin had later Trotsky retouched out of this photograph. (cf. King, 1997). A recent example is reported by Healy (2008) about North Korean leader Kim Jong Il.
|
||||
|
||||
=== Internet sources ===
|
||||
|
||||
Much interest in evaluating Internet sources (such as Wikipedia) is reflected in the scholarly literature of library and information science and in other fields. Mintz (2002) is an edited volume about this issue. Examples of literature examining Internet sources include Chesney (2006), Fritch & Cromwell (2001), Leth & Thurén (2000) and Wilkinson, Bennett, & Oliver (1997).
|
||||
|
||||
=== Archaeology and history ===
|
||||
"In history, the term historical method was first introduced in a systematic way in the sixteenth century by Jean Bodin in his treatise of source criticism, Methodus ad facilem historiarium cognitionem (1566). Characteristically, Bodin's treatise intended to establish the ways by which reliable knowledge of the past could be established by checking sources against one another and by so assessing the reliability of the information conveyed by them, relating them to the interests involved." (Lorenz, 2001, p. 6870).
|
||||
As written above, modern source criticism in history is closely associated with the German historian Leopold von Ranke (1795–1886), who influenced historical methods on both sides of the Atlantic Ocean, although in rather different ways. American history developed in a more empirist and antiphilosophical way (cf., Novick, 1988).
|
||||
Two of the best-known rule books from the 19th century are Bernheim (1889) and Langlois & Seignobos (1898). These books provided a seven-step procedure (here quoted from Howell & Prevenier, 2001, p. 70–71):
|
||||
|
||||
If the sources all agree about an event, historians can consider the event proved.
|
||||
However, majority does not rule; even if most sources relate events in one way, that version will not prevail unless it passes the test of critical textual analysis.
|
||||
The source whose account can be confirmed by reference to outside authorities in some of its parts can be trusted in its entirety if it is impossible similarly to confirm the entire text.
|
||||
When two sources disagree on a particular point, the historian will prefer the source with most "authority"—i.e. the source created by the expert or by the eyewitness.
|
||||
Eyewitnesses are, in general, to be preferred, especially in circumstances where the ordinary observer could have accurately reported what transpired and, more specifically, when they deal with facts known by most contemporaries.
|
||||
If two independently created sources agree on a matter, the reliability of each is measureably enhanced.
|
||||
When two sources disagree (and there is no other means of evaluation), then historians take the source which seems to accord best with common sense.
|
||||
Gudmundsson (2007, p. 38) wrote: "Source criticism should not totally dominate later courses. Other important perspectives, for example, philosophy of history/view of history, should not suffer by being neglected" (Translated by BH). This quote makes a distinction between source criticism on the one hand and historical philosophy on the other hand. However, different views of history and different specific theories about the field being studied may have important consequences for how sources are selected, interpreted and used. Feminist scholars may, for example, select sources made by women and may interpret sources from a feminist perspective. Epistemology should thus be considered a part of source criticism. It is in particular related to "tendency analysis".
|
||||
In archaeology, radiocarbon dating is an important technique to establish the age of information sources. Methods of this kind were the ideal when history established itself as both a scientific discipline and as a profession based on "scientific" principles in the last part of the 1880s (although radiocarbon dating is a more recent example of such methods). The empiricist movement in history brought along both "source criticism" as a research method and also in many countries large scale publishing efforts to make valid editions of "source materials" such as important letters and official documents (e.g. as facsimiles or transcriptions).
|
||||
Historiography and historical method include the study of the reliability of the sources used, in terms of, for example, authorship, credibility of the author, and the authenticity or corruption of the text.
|
||||
|
||||
=== Biblical studies ===
|
||||
|
||||
Source criticism, as the term is used in biblical criticism, refers to the attempt to establish the sources used by the author and/or redactor of the final text. The term "literary criticism" is occasionally used as a synonym.
|
||||
Biblical source criticism originated in the 18th century with the work of Jean Astruc, who adapted the methods already developed for investigating the texts of classical antiquity (Homer's Iliad in particular) to his own investigation into the sources of the Book of Genesis. It was subsequently considerably developed by German scholars in what was known as "the higher criticism", a term no longer in widespread use. The ultimate aim of these scholars was to reconstruct the history of the biblical text, as well as the religious history of ancient Israel.
|
||||
Related to source criticism is redaction criticism which seeks to determine how and why the redactor (editor) put the sources together the way he did. Also related is form criticism and tradition history which try to reconstruct the oral prehistory behind the identified written sources.
|
||||
|
||||
=== Journalism ===
|
||||
|
||||
Journalists often work with strong time pressure and have access to only a limited number of information sources such as news bureaus, persons which may be interviewed, newspapers, journals and so on (see journalism sourcing). Journalists' possibility for conducting serious source criticism is thus limited compared to, for example, historians' possibilities.
|
||||
|
||||
=== Legal studies ===
|
||||
65
data/en.wikipedia.org/wiki/Source_criticism-3.md
Normal file
65
data/en.wikipedia.org/wiki/Source_criticism-3.md
Normal file
@ -0,0 +1,65 @@
|
||||
---
|
||||
title: "Source criticism"
|
||||
chunk: 4/5
|
||||
source: "https://en.wikipedia.org/wiki/Source_criticism"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:54.398591+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The most important legal sources are created by parliaments, governments, courts, and legal researchers. They may be written or informal and based on established practices. Views concerning the quality of sources differ among legal philosophies: Legal positivism is the view that the text of the law should be considered in isolation, while legal realism, interpretivism (legal), critical legal studies and feminist legal criticism interprets the law on a broader cultural basis.
|
||||
|
||||
== See also ==
|
||||
|
||||
== Notes ==
|
||||
|
||||
== References ==
|
||||
American Library Association (1994) Evaluating Information: A Basic Checklist. Brochure. American Library Association
|
||||
Anderson, Richard C. (1978). Schema-directed processes in language comprehension. IN: NATO International Conference on Cognitive Psychology and Instruction, 1977, Amsterdam: Cognitive Psychology and Instruction. Ed. by A. M. Lesgold, J. W. Pellegrino, S. D. Fokkema & R. Glaser. New York: Plenum Press (pp. 67–82).
|
||||
Anderson, Richard C. & Pichert, J. W. (1977). Recall of previously unrecallable information following a shift of perspective. Urbana, Il: University of Illinois, Center for the Study of Reading, April. 1977. (Technical Report 41). Available in full-text from: http://eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/31/83/58.pdf
|
||||
Bazerman, Charles (1995). The Informed Writer: Using Sources in the Disciplines. 5th ed. Houghton Mifflin.
|
||||
Bee, Ronald E. (1983). Statistics and Source Criticism. Vetus Testamentum, Volume 33, Number 4, 483–488.
|
||||
Beecher-Monas, Erica (2007). Evaluating scientific evidence : an interdisciplinary framework for intellectual due process. Cambridge; New York: Cambridge University Press.
|
||||
Bernheim, Ernst (1889). Lehrbuch der Historischen Methode und der Geschichtsphilosophie [Guidebook for Historical Method and the Philosophy of History]. Leipzig: Duncker & Humblot.
|
||||
Brundage, Anthony (2007). Going to the Sources: A Guide to Historical Research and Writing, 4th Ed. Wheeling, Illinois: Harlan Davidson, Inc. (3rd edition, 1989 cited in text above).
|
||||
Chesney, T. (2006). An empirical examination of Wikipedia's credibility. First Monday, 11(11), URL: http://firstmonday.org/issues/issue11_11/chesney/index.html
|
||||
Encyclopædia Britannica (2006). Fatally Flawed. Refuting the recent study on encyclopedic accuracy by the journal Nature. http://corporate.britannica.com/britannica_nature_response.pdf Nature's response March 23, 2006: http://www.nature.com/press_releases/Britannica_response.pdf
|
||||
Engeldinger, Eugene A. (1988) Bibliographic Instruction and Critical Thinking: The Contribution of the Annotated Bibliography. Research Quarterly, Vol. 28, Winter, p. 195–202
|
||||
Engeldinger, Eugene A. (1998) Technology Infrastructure and Information Literacy. Library Philosophy and Practice Vol. 1, No. 1
|
||||
Fritch, J. W., & Cromwell, R. L. (2001). Evaluating Internet resources: Identity, affiliation, and cognitive authority in a networked world. Journal of the American Society for Information Science and Technology, 52, 499–507.
|
||||
Gerhart, Susan L. (2004). Do Web search engines suppress controversy?. First Monday 9(1).
|
||||
Giles, Jim (1 December 2005). "Internet encyclopaedias go head to head". Nature. 438 (7070): 900–901. Bibcode:2005Natur.438..900G. doi:10.1038/438900a. PMID 16355180.
|
||||
Gudmundsson, David (2007). När kritiska elever är målet. Att undervisa i källkritik på gymnasiet. [When the Goal is Critical Students. Teaching Source Criticism in Upper Secondary School]. Malmö, Sweden: Malmö högskola. Full text (in Swedish)
|
||||
Hardtwig, W. (2001). Ranke, Leopold von (1795–1886). IN: Smelser, N. J. & Baltes, P. B. (eds.) International Encyclopedia of the Social and Behavioral Sciences. Amsterdam: Elsevier. (12738–12741).
|
||||
Harris, Ben (1979). Whatever Happened to Little Albert? American Psychologist, 34, 2, pp. 151–160. link to full text
|
||||
Harris, Ben (1980). Ceremonial versus critical history of psychology. American Psychologist, 35(2), 218–219. (Note).
|
||||
Healy, Jack (2008). Was the Dear Leader Photoshopped In? November 7, 2008, 2:57 pm [President Kim Jong Il, North Korea]. http://thelede.blogs.nytimes.com/2008/11/07/was-the-dear-leader-photoshopped-in/?scp=7&sq=Kim%20Jong-il&st=cse
|
||||
Hjørland, Birger (2008). Source criticism. In: Epistemological Lifeboat. Ed. by Birger Hjørland & Jeppe Nicolaisen.
|
||||
Howell, Martha & Prevenier, Walter (2001). From Reliable Sources: An Introduction to Historical Methods. Ithaca: Cornell University Press. ISBN 0-8014-8560-6.
|
||||
Katzer, Jeffrey; Cook, Kenneth H. & Crouch, Wayne W. (1998). Evaluating Information: A Guide for Users of Social Science Research. 4th ed. Boston, MA: McGraw-Hill.
|
||||
King, David (1997) The Commissar Vanishes: the falsification of photographs and art in Stalin's Russia. Metropolitan Books, New York.
|
||||
Langlois, Charles-Victor & Seignobos, Charles (1898). Introduction aux études historiques [Introduction to the Study of History]. Paris: Librairie Hachette. Full text (in French). Introduction to the Study of History Full text (in English)
|
||||
Leth, Göran & Thurén, Torsten (2000). Källkritik för internet . Stockholm: Styrelsen för Psykologiskt Försvar. (Retrieved 2007-11-30).
|
||||
Loftus, Elizabeth F. (1996). Eyewitness Testimony. Revised edition Cambridge, MA: Harward University Press. (Original edition:1979).
|
||||
Lorenz, C. (2001). History: Theories and Methods. IN: Smelser, N. J. & Baltes, P. B. (eds.) International Encyclopedia of the Social and Behavioral Sciences. Amsterdam: Elsevier. (Pp. 6869–6876).
|
||||
Mathewson, Daniel B. (2002). A critical binarism: Source criticism and deconstructive criticism. Journal for the Study of the Old Testament no98, pp. 3–28. Abstract: When classifying the array of interpretive methods currently available, biblical critics regularly distinguish between historical-critical methods, on the one hand, and literary critical methods, on the other. Frequently, methods on one side of the divide are said to be antagonistic to certain methods on the other. This article examines two such presumed antagonistic methods, source criticism and deconstructive criticism, and argues that they are not, in fact, antagonistic, but similar: both are postmodern movements, and both share an interpretive methodology (insofar as it is correct to speak of a deconstructive methodology). This argument is illustrated with a source-critical and a deconstructive reading of Exodus 14.
|
||||
Mattus, Maria (2007). Finding Credible Information: A Challenge to Students Writing Academic Essays. Human IT 9(2), 1–28. Retrieved 2007-09-04 from: [1]
|
||||
Meola, Marc (9 July 2004). "Chucking the Checklist: A Contextual Approach to Teaching Undergraduates Web-Site Evaluation". Portal: Libraries and the Academy. 4 (3): 331–344. doi:10.1353/pla.2004.0055. S2CID 62630665.
|
||||
Mintz, Anne P. (ed.). (2002). Web of deception. Misinformation on the Internet. Medford, NJ: Information Today.
|
||||
Müller, Philipp (2009). Understanding history: Hermeneutics and source-criticism in historical scholarship. IN: Dobson, Miriam & Ziemann, Benjamin (eds): Reading primary sources. The interpretation of texts from nineteenth and twentieth-century history. London: Routledge (pp. 21–36).
|
||||
Olden-Jørgensen, Sebastian (2001). Til Kilderne: Introduktion til Historisk Kildekritik (in Danish). [To the sources: Introduction to historical source criticism]. København: Gads Forlag. ISBN 978-87-12-03778-1.
|
||||
Reinfandt, Christohp (2009). Reading texts after the linguistic turn: approaches from literary studies and their implementation. IN: Dobson, Miriam & Ziemann, Benjamin (eds): Reading primary sources. The interpretation of texts from nineteenth and twentieth-century history. London: Routledge (pp. 37–54).
|
||||
Rieh, S. Y. (2002). Judgment of information quality and cognitive authority in the Web. Journal of the American Society for Information Science and Technology, 53(2), 145–161. https://web.archive.org/web/20090731152623/http://www.si.umich.edu/rieh/papers/rieh_jasist2002.pdf
|
||||
Rieh, S. Y. (2005). Cognitive authority. I: K. E. Fisher, S. Erdelez, & E. F. McKechnie (Eds.), Theories of information behavior: A researchers' guide . Medford, NJ: Information Today (pp. 83–87). https://web.archive.org/web/20080512170752/http://newweb2.si.umich.edu/rieh/papers/rieh_IBTheory.pdf
|
||||
Rieh, Soo Young & Danielson, David R. (2007). Credibility: A multidisciplinary framework. Annual Review of Information Science and Technology, 41, 307–364.
|
||||
Riegelman, Richard K. (2004). Studying a Study and Testing a Test: How to Read the Medical Evidence. 5th ed. Philadelphia, PA: Lippincott Williams & Wilkins.
|
||||
Savolainen, R. (2007). Media credibility and cognitive authority. The case of seeking orienting information. Information Research, 12(3) paper 319. Available at https://web.archive.org/web/20180416064908/http://www.informationr.net/ir///12-3/paper319.html
|
||||
Slife, Brent D. & Williams, R. N. (1995). What's behind the research? Discovering hidden assumptions in the behavioral sciences. Thousand Oaks, CA: Sage Publications. ("A Consumers Guide to the Behavioral Sciences").
|
||||
Taylor, John (1991). War photography; realism in the British press. London : Routledge.
|
||||
Thurén, Torsten. (1997). Källkritik. Stockholm: Almqvist & Wiksell.
|
||||
Walton, Douglas (1998). Fallacies. IN: Routledge Encyclopedia of Philosophy, Version 1.0, London: Routledge
|
||||
Webb, E J; Campbell, D T; Schwartz, R D & Sechrest, L (2000). Unobtrusive measures; revised edition. Sage Publications Inc.
|
||||
"Wikipedia: Testsieg und Verschwörungen" [Sterns test of Wikipedia]. Heise Online (in German). 5 December 2007.
|
||||
Wilkinson, G.L., Bennett, L.T., & Oliver, K.M. (1997). Evaluation criteria and indicators of quality for Internet resources. Educational Technology, 37(3), 52–59.
|
||||
Wilson, Patrick (1983). Second-Hand Knowledge. An Inquiry into Cognitive Authority. Westport, Conn.: Greenwood.
|
||||
14
data/en.wikipedia.org/wiki/Source_criticism-4.md
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14
data/en.wikipedia.org/wiki/Source_criticism-4.md
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|
||||
---
|
||||
title: "Source criticism"
|
||||
chunk: 5/5
|
||||
source: "https://en.wikipedia.org/wiki/Source_criticism"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:54.398591+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
== External links ==
|
||||
|
||||
The Source Compass: Source Criticism
|
||||
The History Sourcebook: The Need for Source Criticism
|
||||
27
data/en.wikipedia.org/wiki/Statistical_alchemy-0.md
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27
data/en.wikipedia.org/wiki/Statistical_alchemy-0.md
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|
||||
---
|
||||
title: "Statistical alchemy"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Statistical_alchemy"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:55.564146+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Statistical alchemy was a term originated by John Maynard Keynes to describe econometrics in 1939.
|
||||
The phrase has subsequently been used by Alvan Feinstein to describe meta-analysis. It is generally regarded as a deprecatory term which undermines attempts to present such activities as meeting the rigorous standards of science.
|
||||
|
||||
|
||||
== Econometrics ==
|
||||
Keynes (1939) wrote a review of Jan Tinbergen's Statistical Testing of Business-Cycle Theories. Although he praised Tinbergen for his objectivity, he however depicted his methodology as "black magic" which he regarded as essentially untrustworthy. He was unpersuaded that "this brand of statistical alchemy is ripe to become a branch of science" (emphasis in the original).
|
||||
Often this metaphor is seen as a way of suggesting that econometricians were following a foolhardy pursuit comparable to the alchemical quest of turning base metal into gold. However G. M. P. Swann points out that Keynes was well aware that such eminent early scientists as Isaac Newton. He rather proposes a more nuanced interpretation of the metaphor as referring to the Alkahest, a universal solvent, which, it was claimed could turn stone into water. He claimed that by restricting econometrics to theory, mathematics and statistics, econometricians had discarded other important applied techniques. Although Ragnar Frisch had made warnings about this, these had been subsequently ignored by other econometricians who had ended up claiming that econometrics constituted a universal solvent.
|
||||
|
||||
|
||||
== Meta-analysis ==
|
||||
Feinstein (1995) published "Meta-analysis: statistical alchemy for the 21st century" where he claimed that in meta-analysis scientific requirements had been removed or destroyed, eliminating the scientific requirements of reproducibility and precision. This was equivalent to a free lunch, comparable to the alchemical transmutation of base metals to gold. Detourning the adage concerning the combination of apples and oranges, Feinstein suggested that meta-analytic mixtures were so heterogeneous that they might be better described as "combining rotten fruits". He argues that meta-analysis violates the Bradford Hill criteria of consistency as inconsistencies are ignored or buried through the process of agglomerating the data.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
Feinstein, A (Jan 1995). "Meta-analysis: statistical alchemy for the 21st century". Journal of Clinical Epidemiology. 48 (1): 71–9. doi:10.1016/0895-4356(94)00110-c. PMID 7853050.
|
||||
Keynes, J. M. (1939). "'Professor Tinbergen's method'". Economic Journal. 49 (195): 558–568. doi:10.1093/ej/49.195.558.
|
||||
41
data/en.wikipedia.org/wiki/Statistical_inference-0.md
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data/en.wikipedia.org/wiki/Statistical_inference-0.md
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|
||||
---
|
||||
title: "Statistical inference"
|
||||
chunk: 1/4
|
||||
source: "https://en.wikipedia.org/wiki/Statistical_inference"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:56.828887+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.
|
||||
Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In machine learning, the term inference is sometimes used instead to mean "make a prediction, by evaluating an already trained model"; in this context inferring properties of the model is referred to as training or learning (rather than inference), and using a model for prediction is referred to as inference (instead of prediction); see also predictive inference.
|
||||
|
||||
== Introduction ==
|
||||
Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling. Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.
|
||||
Konishi and Kitagawa state "The majority of the problems in statistical inference can be considered to be problems related to statistical modeling". Relatedly, Sir David Cox has said, "How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis".
|
||||
The conclusion of a statistical inference is a statistical proposition. Some common forms of statistical proposition are the following:
|
||||
|
||||
a point estimate, i.e. a particular value that best approximates some parameter of interest;
|
||||
an interval estimate, e.g. a confidence interval (or set estimate). A confidence interval is an interval constructed using data from a sample, such that if the procedure were repeated over many independent samples (mathematically, by taking the limit), a fixed proportion (e.g., 95% for a 95% confidence interval) of the resulting intervals would contain the true value of the parameter, i.e., the population parameter;
|
||||
a credible interval, i.e. a set of values containing, for example, 95% of posterior belief;
|
||||
rejection of a hypothesis;
|
||||
clustering or classification of data points into groups.
|
||||
|
||||
== Models and assumptions ==
|
||||
|
||||
Any statistical inference requires some assumptions. A statistical model is a set of assumptions concerning the generation of the observed data and similar data. Descriptions of statistical models usually emphasize the role of population quantities of interest, about which we wish to draw inference. Descriptive statistics are typically used as a preliminary step before more formal inferences are drawn.
|
||||
|
||||
=== Degree of models/assumptions ===
|
||||
Statisticians distinguish between three levels of modeling assumptions:
|
||||
|
||||
Fully parametric: The probability distributions describing the data-generation process are assumed to be fully described by a family of probability distributions involving only a finite number of unknown parameters. For example, one may assume that the distribution of population values is truly Normal, with unknown mean and variance, and that datasets are generated by 'simple' random sampling. The family of generalized linear models is a widely used and flexible class of parametric models.
|
||||
Non-parametric: The assumptions made about the process generating the data are much less than in parametric statistics and may be minimal. For example, every continuous probability distribution has a median, which may be estimated using the sample median or the Hodges–Lehmann–Sen estimator, which has good properties when the data arise from simple random sampling.
|
||||
Semi-parametric: This term typically implies assumptions 'in between' fully and non-parametric approaches. For example, one may assume that a population distribution has a finite mean. Furthermore, one may assume that the mean response level in the population depends in a truly linear manner on some covariate (a parametric assumption) but not make any parametric assumption describing the variance around that mean (i.e. about the presence or possible form of any heteroscedasticity). More generally, semi-parametric models can often be separated into 'structural' and 'random variation' components. One component is treated parametrically and the other non-parametrically. The well-known Cox model is a set of semi-parametric assumptions.
|
||||
|
||||
=== Importance of valid models/assumptions ===
|
||||
|
||||
Whatever level of assumption is made, correctly calibrated inference, in general, requires these assumptions to be correct; i.e. that the data-generating mechanisms really have been correctly specified.
|
||||
Incorrect assumptions of 'simple' random sampling can invalidate statistical inference. More complex semi- and fully parametric assumptions are also cause for concern. For example, incorrectly assuming the Cox model can in some cases lead to faulty conclusions. Incorrect assumptions of Normality in the population also invalidates some forms of regression-based inference. The use of any parametric model is viewed skeptically by most experts in sampling human populations: "most sampling statisticians, when they deal with confidence intervals at all, limit themselves to statements about [estimators] based on very large samples, where the central limit theorem ensures that these [estimators] will have distributions that are nearly normal." In particular, a normal distribution "would be a totally unrealistic and catastrophically unwise assumption to make if we were dealing with any kind of economic population." Here, the central limit theorem states that the distribution of the sample mean "for very large samples" is approximately normally distributed, if the distribution is not heavy-tailed.
|
||||
|
||||
==== Approximate distributions ====
|
||||
271
data/en.wikipedia.org/wiki/Statistical_inference-1.md
Normal file
271
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@ -0,0 +1,271 @@
|
||||
---
|
||||
title: "Statistical inference"
|
||||
chunk: 2/4
|
||||
source: "https://en.wikipedia.org/wiki/Statistical_inference"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:56.828887+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Given the difficulty in specifying exact distributions of sample statistics, many methods have been developed for approximating these.
|
||||
With finite samples, approximation results measure how close a limiting distribution approaches the statistic's sample distribution: For example, with 10,000 independent samples the normal distribution approximates (to two digits of accuracy) the distribution of the sample mean for many population distributions, by the Berry–Esseen theorem. Yet for many practical purposes, the normal approximation provides a good approximation to the sample-mean's distribution when there are 10 (or more) independent samples, according to simulation studies and statisticians' experience. Following Kolmogorov's work in the 1950s, advanced statistics uses approximation theory and functional analysis to quantify the error of approximation. In this approach, the metric geometry of probability distributions is studied; this approach quantifies approximation error with, for example, the Kullback–Leibler divergence, Bregman divergence, and the Hellinger distance.
|
||||
With indefinitely large samples, limiting results like the central limit theorem describe the sample statistic's limiting distribution if one exists. Limiting results are not statements about finite samples, and indeed are irrelevant to finite samples. However, the asymptotic theory of limiting distributions is often invoked for work with finite samples. For example, limiting results are often invoked to justify the generalized method of moments and the use of generalized estimating equations, which are popular in econometrics and biostatistics. The magnitude of the difference between the limiting distribution and the true distribution (formally, the 'error' of the approximation) can be assessed using simulation. The heuristic application of limiting results to finite samples is common practice in many applications, especially with low-dimensional models with log-concave likelihoods (such as with one-parameter exponential families).
|
||||
|
||||
=== Randomization-based models ===
|
||||
|
||||
For a given dataset that was produced by a randomization design, the randomization distribution of a statistic (under the null-hypothesis) is defined by evaluating the test statistic for all of the plans that could have been generated by the randomization design. In frequentist inference, the randomization allows inferences to be based on the randomization distribution rather than a subjective model, and this is important especially in survey sampling and design of experiments. Statistical inference from randomized studies is also more straightforward than many other situations. In Bayesian inference, randomization is also of importance: in survey sampling, use of sampling without replacement ensures the exchangeability of the sample with the population; in randomized experiments, randomization warrants a missing at random assumption for covariate information.
|
||||
Objective randomization allows properly inductive procedures. Many statisticians prefer randomization-based analysis of data that was generated by well-defined randomization procedures. (However, it is true that in fields of science with developed theoretical knowledge and experimental control, randomized experiments may increase the costs of experimentation without improving the quality of inferences.) Similarly, results from randomized experiments are recommended by leading statistical authorities as allowing inferences with greater reliability than do observational studies of the same phenomena. However, a good observational study may be better than a bad randomized experiment.
|
||||
The statistical analysis of a randomized experiment may be based on the randomization scheme stated in the experimental protocol and does not need a subjective model.
|
||||
However, at any time, some hypotheses cannot be tested using objective statistical models, which accurately describe randomized experiments or random samples. In some cases, such randomized studies are uneconomical or unethical.
|
||||
|
||||
==== Model-based analysis of randomized experiments ====
|
||||
It is standard practice to refer to a statistical model, e.g., a linear or logistic models, when analyzing data from randomized experiments. However, the randomization scheme guides the choice of a statistical model. It is not possible to choose an appropriate model without knowing the randomization scheme. Seriously misleading results can be obtained analyzing data from randomized experiments while ignoring the experimental protocol; common mistakes include forgetting the blocking used in an experiment and confusing repeated measurements on the same experimental unit with independent replicates of the treatment applied to different experimental units.
|
||||
|
||||
==== Model-free randomization inference ====
|
||||
Model-free techniques provide a complement to model-based methods, which employ reductionist strategies of reality-simplification. The former combine, evolve, ensemble and train algorithms dynamically adapting to the contextual affinities of a process and learning the intrinsic characteristics of the observations.
|
||||
For example, model-free simple linear regression is based either on:
|
||||
|
||||
a random design, where the pairs of observations
|
||||
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|
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|
||||
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||||
are deterministic, but the corresponding response variables
|
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|
||||
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||||
are random and independent with a common conditional distribution, i.e.,
|
||||
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||||
|
||||
x
|
||||
|
||||
|
||||
(
|
||||
y
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle P\left(Y_{j}\leq y|X_{j}=x\right)=D_{x}(y)}
|
||||
|
||||
, which is independent of the index
|
||||
|
||||
|
||||
|
||||
j
|
||||
|
||||
|
||||
{\displaystyle j}
|
||||
|
||||
.
|
||||
In either case, the model-free randomization inference for features of the common conditional distribution
|
||||
|
||||
|
||||
|
||||
|
||||
D
|
||||
|
||||
x
|
||||
|
||||
|
||||
(
|
||||
.
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle D_{x}(.)}
|
||||
|
||||
relies on some regularity conditions, e.g. functional smoothness. For instance, model-free randomization inference for the population feature conditional mean,
|
||||
|
||||
|
||||
|
||||
μ
|
||||
(
|
||||
x
|
||||
)
|
||||
=
|
||||
E
|
||||
(
|
||||
Y
|
||||
|
||||
|
|
||||
|
||||
X
|
||||
=
|
||||
x
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle \mu (x)=E(Y|X=x)}
|
||||
|
||||
, can be consistently estimated via local averaging or local polynomial fitting, under the assumption that
|
||||
|
||||
|
||||
|
||||
μ
|
||||
(
|
||||
x
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle \mu (x)}
|
||||
|
||||
is smooth. Also, relying on asymptotic normality or resampling, we can construct confidence intervals for the population feature, in this case, the conditional mean,
|
||||
|
||||
|
||||
|
||||
μ
|
||||
(
|
||||
x
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle \mu (x)}
|
||||
|
||||
.
|
||||
|
||||
== Paradigms for inference ==
|
||||
Different schools of statistical inference have become established. These schools—or "paradigms"—are not mutually exclusive, and methods that work well under one paradigm often have attractive interpretations under other paradigms.
|
||||
Bandyopadhyay and Forster describe four paradigms: The classical (or frequentist) paradigm, the Bayesian paradigm, the likelihoodist paradigm, and the Akaikean-Information Criterion-based paradigm.
|
||||
|
||||
=== Frequentist inference ===
|
||||
71
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71
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@ -0,0 +1,71 @@
|
||||
---
|
||||
title: "Statistical inference"
|
||||
chunk: 3/4
|
||||
source: "https://en.wikipedia.org/wiki/Statistical_inference"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:56.828887+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
This paradigm calibrates the plausibility of propositions by considering (notional) repeated sampling of a population distribution to produce datasets similar to the one at hand. By considering the dataset's characteristics under repeated sampling, the frequentist properties of a statistical proposition can be quantified—although in practice this quantification may be challenging.
|
||||
|
||||
==== Examples of frequentist inference ====
|
||||
p-value
|
||||
Confidence interval
|
||||
Null hypothesis significance testing
|
||||
|
||||
==== Frequentist inference, objectivity, and decision theory ====
|
||||
One interpretation of frequentist inference (or classical inference) is that it is applicable only in terms of frequency probability; that is, in terms of repeated sampling from a population. However, the approach of Neyman develops these procedures in terms of pre-experiment probabilities. That is, before undertaking an experiment, one decides on a rule for coming to a conclusion such that the probability of being correct is controlled in a suitable way: such a probability need not have a frequentist or repeated sampling interpretation. In contrast, Bayesian inference works in terms of conditional probabilities (i.e. probabilities conditional on the observed data), compared to the marginal (but conditioned on unknown parameters) probabilities used in the frequentist approach.
|
||||
The frequentist procedures of significance testing and confidence intervals can be constructed without regard to utility functions. However, some elements of frequentist statistics, such as statistical decision theory, do incorporate utility functions. In particular, frequentist developments of optimal inference (such as minimum-variance unbiased estimators, or uniformly most powerful testing) make use of loss functions, which play the role of (negative) utility functions. Loss functions need not be explicitly stated for statistical theorists to prove that a statistical procedure has an optimality property. However, loss-functions are often useful for stating optimality properties: for example, median-unbiased estimators are optimal under absolute value loss functions, in that they minimize expected loss, and least squares estimators are optimal under squared error loss functions, in that they minimize expected loss.
|
||||
While statisticians using frequentist inference must choose for themselves the parameters of interest, and the estimators/test statistic to be used, the absence of obviously explicit utilities and prior distributions has helped frequentist procedures to become widely viewed as 'objective'.
|
||||
|
||||
=== Bayesian inference ===
|
||||
|
||||
The Bayesian calculus describes degrees of belief using the 'language' of probability; beliefs are positive, integrate into one, and obey probability axioms. Bayesian inference uses the available posterior beliefs as the basis for making statistical propositions. There are several different justifications for using the Bayesian approach.
|
||||
|
||||
==== Examples of Bayesian inference ====
|
||||
Credible interval for interval estimation
|
||||
Bayes factors for model comparison
|
||||
|
||||
==== Bayesian inference, subjectivity and decision theory ====
|
||||
Many informal Bayesian inferences are based on "intuitively reasonable" summaries of the posterior. For example, the posterior mean, median and mode, highest posterior density intervals, and Bayes Factors can all be motivated in this way. While a user's utility function need not be stated for this sort of inference, these summaries do all depend (to some extent) on stated prior beliefs, and are generally viewed as subjective conclusions. (Methods of prior construction which do not require external input have been proposed but not yet fully developed.)
|
||||
Formally, Bayesian inference is calibrated with reference to an explicitly stated utility, or loss function; the 'Bayes rule' is the one which maximizes expected utility, averaged over the posterior uncertainty. Formal Bayesian inference therefore automatically provides optimal decisions in a decision theoretic sense. Given assumptions, data and utility, Bayesian inference can be made for essentially any problem, although not every statistical inference need have a Bayesian interpretation. Analyses which are not formally Bayesian can be (logically) incoherent; a feature of Bayesian procedures which use proper priors (i.e. those integrable to one) is that they are guaranteed to be coherent. Some advocates of Bayesian inference assert that inference must take place in this decision-theoretic framework, and that Bayesian inference should not conclude with the evaluation and summarization of posterior beliefs.
|
||||
|
||||
=== Likelihood-based inference ===
|
||||
Likelihood-based inference is a paradigm used to estimate the parameters of a statistical model based on observed data. Likelihoodism approaches statistics by using the likelihood function, denoted as
|
||||
|
||||
|
||||
|
||||
L
|
||||
(
|
||||
x
|
||||
|
||||
|
|
||||
|
||||
θ
|
||||
)
|
||||
|
||||
|
||||
{\displaystyle L(x|\theta )}
|
||||
|
||||
, quantifies the probability of observing the given data
|
||||
|
||||
|
||||
|
||||
x
|
||||
|
||||
|
||||
{\displaystyle x}
|
||||
|
||||
, assuming a specific set of parameter values
|
||||
|
||||
|
||||
|
||||
θ
|
||||
|
||||
|
||||
{\displaystyle \theta }
|
||||
|
||||
. In likelihood-based inference, the goal is to find the set of parameter values that maximizes the likelihood function, or equivalently, maximizes the probability of observing the given data.
|
||||
The process of likelihood-based inference usually involves the following steps:
|
||||
107
data/en.wikipedia.org/wiki/Statistical_inference-3.md
Normal file
107
data/en.wikipedia.org/wiki/Statistical_inference-3.md
Normal file
@ -0,0 +1,107 @@
|
||||
---
|
||||
title: "Statistical inference"
|
||||
chunk: 4/4
|
||||
source: "https://en.wikipedia.org/wiki/Statistical_inference"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:56.828887+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Formulating the statistical model: A statistical model is defined based on the problem at hand, specifying the distributional assumptions and the relationship between the observed data and the unknown parameters. The model can be simple, such as a normal distribution with known variance, or complex, such as a hierarchical model with multiple levels of random effects.
|
||||
Constructing the likelihood function: Given the statistical model, the likelihood function is constructed by evaluating the joint probability density or mass function of the observed data as a function of the unknown parameters. This function represents the probability of observing the data for different values of the parameters.
|
||||
Maximizing the likelihood function: The next step is to find the set of parameter values that maximizes the likelihood function. This can be achieved using optimization techniques such as numerical optimization algorithms. The estimated parameter values, often denoted as
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
y
|
||||
¯
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
{\displaystyle {\bar {y}}}
|
||||
|
||||
, are the maximum likelihood estimates (MLEs).
|
||||
Assessing uncertainty: Once the MLEs are obtained, it is crucial to quantify the uncertainty associated with the parameter estimates. This can be done by calculating standard errors, confidence intervals, or conducting hypothesis tests based on asymptotic theory or simulation techniques such as bootstrapping.
|
||||
Model checking: After obtaining the parameter estimates and assessing their uncertainty, it is important to assess the adequacy of the statistical model. This involves checking the assumptions made in the model and evaluating the fit of the model to the data using goodness-of-fit tests, residual analysis, or graphical diagnostics.
|
||||
Inference and interpretation: Finally, based on the estimated parameters and model assessment, statistical inference can be performed. This involves drawing conclusions about the population parameters, making predictions, or testing hypotheses based on the estimated model.
|
||||
|
||||
=== AIC-based inference ===
|
||||
|
||||
The Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection.
|
||||
AIC is founded on information theory: it offers an estimate of the relative information lost when a given model is used to represent the process that generated the data. (In doing so, it deals with the trade-off between the goodness of fit of the model and the simplicity of the model.)
|
||||
|
||||
=== Other paradigms for inference ===
|
||||
|
||||
==== Minimum description length ====
|
||||
|
||||
The minimum description length (MDL) principle has been developed from ideas in information theory and the theory of Kolmogorov complexity. The (MDL) principle selects statistical models that maximally compress the data; inference proceeds without assuming counterfactual or non-falsifiable "data-generating mechanisms" or probability models for the data, as might be done in frequentist or Bayesian approaches.
|
||||
However, if a "data generating mechanism" does exist in reality, then according to Shannon's source coding theorem it provides the MDL description of the data, on average and asymptotically. In minimizing description length (or descriptive complexity), MDL estimation is similar to maximum likelihood estimation and maximum a posteriori estimation (using maximum-entropy Bayesian priors). However, MDL avoids assuming that the underlying probability model is known; the MDL principle can also be applied without assumptions that e.g. the data arose from independent sampling.
|
||||
The MDL principle has been applied in communication-coding theory in information theory, in linear regression, and in data mining.
|
||||
The evaluation of MDL-based inferential procedures often uses techniques or criteria from computational complexity theory.
|
||||
|
||||
==== Fiducial inference ====
|
||||
|
||||
Fiducial inference was an approach to statistical inference based on fiducial probability, also known as a "fiducial distribution". In subsequent work, this approach has been called ill-defined, extremely limited in applicability, and even fallacious. However this argument is the same as that which shows that a so-called confidence distribution is not a valid probability distribution and, since this has not invalidated the application of confidence intervals, it does not necessarily invalidate conclusions drawn from fiducial arguments. An attempt was made to reinterpret the early work of Fisher's fiducial argument as a special case of an inference theory using upper and lower probabilities.
|
||||
|
||||
==== Structural inference ====
|
||||
Developing ideas of Fisher and of Pitman from 1938 to 1939, George A. Barnard developed "structural inference" or "pivotal inference", an approach using invariant probabilities on group families. Barnard reformulated the arguments behind fiducial inference on a restricted class of models on which "fiducial" procedures would be well-defined and useful. Donald A. S. Fraser developed a general theory for structural inference based on group theory and applied this to linear models. The theory formulated by Fraser has close links to decision theory and Bayesian statistics and can provide optimal frequentist decision rules if they exist.
|
||||
|
||||
== Inference topics ==
|
||||
The topics below are usually included in the area of statistical inference.
|
||||
|
||||
Statistical assumptions
|
||||
Statistical decision theory
|
||||
Estimation theory
|
||||
Statistical hypothesis testing
|
||||
Revising opinions in statistics
|
||||
Design of experiments, the analysis of variance, and regression
|
||||
Survey sampling
|
||||
Summarizing statistical data
|
||||
|
||||
== Predictive inference ==
|
||||
Predictive inference is an approach to statistical inference that emphasizes the prediction of future observations based on past observations.
|
||||
Initially, predictive inference was based on observable parameters and it was the main purpose of studying probability, but it fell out of favor in the 20th century due to a new parametric approach pioneered by Bruno de Finetti. The approach modeled phenomena as a physical system observed with error (e.g., celestial mechanics). De Finetti's idea of exchangeability—that future observations should behave like past observations—came to the attention of the English-speaking world with the 1974 translation from French of his 1937 paper, and has since been propounded by such statisticians as Seymour Geisser.
|
||||
|
||||
== See also ==
|
||||
Algorithmic inference
|
||||
Induction (philosophy)
|
||||
Informal inferential reasoning
|
||||
Information field theory
|
||||
Population proportion
|
||||
Philosophy of statistics
|
||||
Prediction interval
|
||||
Predictive analytics
|
||||
Predictive modelling
|
||||
Stylometry
|
||||
|
||||
== Notes ==
|
||||
|
||||
== References ==
|
||||
|
||||
=== Citations ===
|
||||
|
||||
=== Sources ===
|
||||
|
||||
== Further reading ==
|
||||
Casella, G., Berger, R. L. (2002). Statistical Inference. Duxbury Press. ISBN 0-534-24312-6
|
||||
Freedman, D.A. (1991). "Statistical models and shoe leather". Sociological Methodology. 21: 291–313. doi:10.2307/270939. JSTOR 270939.
|
||||
Held L., Bové D.S. (2014). Applied Statistical Inference—Likelihood and Bayes (Springer).
|
||||
Lenhard, Johannes (2006). "Models and Statistical Inference: the controversy between Fisher and Neyman–Pearson" (PDF). British Journal for the Philosophy of Science. 57: 69–91. doi:10.1093/bjps/axi152. S2CID 14136146.
|
||||
Lindley, D (1958). "Fiducial distribution and Bayes' theorem". Journal of the Royal Statistical Society, Series B. 20: 102–7. doi:10.1111/j.2517-6161.1958.tb00278.x.
|
||||
Rahlf, Thomas (2014). "Statistical Inference", in Claude Diebolt, and Michael Haupert (eds.), "Handbook of Cliometrics ( Springer Reference Series)", Berlin/Heidelberg: Springer.
|
||||
Reid, N.; Cox, D. R. (2014). "On Some Principles of Statistical Inference". International Statistical Review. 83 (2): 293–308. doi:10.1111/insr.12067. hdl:10.1111/insr.12067. S2CID 17410547.
|
||||
Sagitov, Serik (2022). "Statistical Inference". Wikibooks. http://upload.wikimedia.org/wikipedia/commons/f/f9/Statistical_Inference.pdf
|
||||
Young, G.A., Smith, R.L. (2005). Essentials of Statistical Inference, CUP. ISBN 0-521-83971-8
|
||||
|
||||
== External links ==
|
||||
|
||||
Statistical Inference – lecture on the MIT OpenCourseWare platform
|
||||
Statistical Inference – lecture by the National Programme on Technology Enhanced Learning
|
||||
An online, Bayesian (MCMC) demo/calculator is available at causaScientia
|
||||
Statistical Inference – interactive Coggle diagram
|
||||
61
data/en.wikipedia.org/wiki/Stigler's_law_of_eponymy-0.md
Normal file
61
data/en.wikipedia.org/wiki/Stigler's_law_of_eponymy-0.md
Normal file
@ -0,0 +1,61 @@
|
||||
---
|
||||
title: "Stigler's law of eponymy"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Stigler's_law_of_eponymy"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:17:58.015991+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Stigler's law of eponymy, proposed by University of Chicago statistics professor Stephen Stigler in 1980, states that no scientific discovery is named after its original discoverer. Examples include Hubble's law, which was derived by Georges Lemaître two years before Edwin Hubble; the Pythagorean theorem, which was known to Babylonian mathematicians and to Indian mathematicians before Pythagoras; and Halley's Comet, which was observed by astronomers since at least 240 BC (although its official designation is due to the first ever mathematical prediction of such astronomical phenomenon in the sky, not to its discovery).
|
||||
Stigler attributed the discovery of Stigler's law to sociologist Robert K. Merton, making "Stigler's law" an example of Stigler's law.
|
||||
|
||||
In his paper, Stigler wrote: I have chosen as a title for this paper, and for the thesis I wish to present and discuss, "Stigler's law of eponymy". At first glance this may appear to be a flagrant violation of the "Institutional Norm of Humility," and since statisticians are even more aware of the importance of norms than are members of other disciplines, I hasten to add a humble disclaimer. If there is an idea in this paper that is not at least implicit in Merton's The Sociology of Science, it is either a happy accident or a likely error. Rather I have, in the Mertonian tradition of the self-confirming hypothesis, attempted to frame the self-proving theorem. For "Stigler's Law of Eponymy" in its simplest form is this: "No scientific discovery is named after its original discoverer."
|
||||
The same observation had previously also been made by many others.
|
||||
|
||||
|
||||
== Derivation ==
|
||||
Historical acclaim for discoveries is often assigned to persons of note who bring attention to an idea that is not yet widely known, whether or not that person was its original inventor – theories may be named long after their discovery. In the case of eponymy, the idea becomes named after that person, even if that person is acknowledged by historians of science not to be the one who discovered it. Often, several people will arrive at a new idea around the same time, as in the case of calculus. It can be dependent on the publicity of the new work and the fame of its publisher as to whether the scientist's name becomes historically associated.
|
||||
|
||||
|
||||
== Similar concepts ==
|
||||
There is a similar quote attributed to Mark Twain:It takes a thousand men to invent a telegraph, or a steam engine, or a phonograph, or a photograph, or a telephone or any other important thing—and the last man gets the credit and we forget the others. He added his little mite—that is all he did. These object lessons should teach us that ninety-nine parts of all things that proceed from the intellect are plagiarisms, pure and simple; and the lesson ought to make us modest. But nothing can do that.
|
||||
Stephen Stigler's father, the economist George Stigler, also examined the process of discovery in economics. He said, "If an earlier, valid statement of a theory falls on deaf ears, and a later restatement is accepted by the science, this is surely proof that the science accepts ideas only when they fit into the then-current state of the science." He gave several examples in which the original discoverer was not recognized as such. Similar arguments were made in regards to accepted ideas relative to the state of science by Thomas Kuhn in The Structure of Scientific Revolutions.
|
||||
|
||||
The Matthew effect was coined by Robert K. Merton to describe how eminent scientists get more credit than a comparatively unknown researcher, even if their work is similar, so that credit will usually be given to researchers who are already famous. Merton notes: This pattern of recognition, skewed in favor of the established scientist, appears principally
|
||||
(i) in cases of collaboration and
|
||||
|
||||
(ii) in cases of independent multiple discoveries made by scientists of distinctly different rank.
|
||||
The effect applies specifically to women through the Matilda effect.
|
||||
Boyer's law was named by Hubert Kennedy in 1972. It says, "Mathematical formulas and theorems are usually not named after their original discoverers" and was named after Carl Boyer, whose book A History of Mathematics contains many examples of this law. Kennedy observed that "it is perhaps interesting to note that this is probably a rare instance of a law whose statement confirms its own validity".
|
||||
"Everything of importance has been said before by somebody who did not discover it" is an adage attributed to Alfred North Whitehead.
|
||||
Russian mathematician Vladimir Arnold wrote in 1998:
|
||||
|
||||
Similarly to the fact that America does not carry Columbus's name, mathematical results are almost never called by the names of their discoverers. […..] Prof. M. Berry once formulated the following two principles:
|
||||
The Arnold principle. If a notion bears a personal name, then this name is not the name of the discoverer.
|
||||
The Berry principle. The Arnold Principle is applicable to itself.
|
||||
|
||||
|
||||
== List of examples ==
|
||||
|
||||
|
||||
== See also ==
|
||||
List of misnamed theorems
|
||||
List of persons considered father or mother of a scientific field
|
||||
Obliteration by incorporation
|
||||
Scientific priority – Credit for first discovery
|
||||
Standing on the shoulders of giants – Metaphor acknowledging past thinkers
|
||||
Theories and sociology of the history of science – Diachronically-comparative sociological analysis of scienticityPages displaying short descriptions of redirect targets
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== Further reading ==
|
||||
Stigler, George J. (1982a). The Economist as Preacher, and Other Essays. Chicago: The University of Chicago Press. ISBN 0-226-77430-9.
|
||||
Stigler, Stephen M. (1980). Gieryn, F. (ed.). "Stigler's Law of Eponymy". Transactions of the New York Academy of Sciences. 39: 147–58. doi:10.1111/j.2164-0947.1980.tb02775.x. (Festschrift for Robert K. Merton)
|
||||
Stigler, Stephen M. (1983). "Who discovered Bayes's theorem?". The American Statistician. 37 (4): 290–6. doi:10.2307/2682766. JSTOR 2682766.
|
||||
Kern, Scott E. (September–October 2002). "Whose Hypothesis? Ciphering, Sectorials, D Lesions, Freckles and the Operation of Stigler's Law". Cancer Biology & Therapy. 1 (5). Landes Bioscience: 571–581. doi:10.4161/cbt.1.5.225. ISSN 1555-8576. PMID 12496492. Retrieved 28 March 2009.
|
||||
See Miller, Jeff. "Eponymy and Laws of Eponymy". on Miller, Jeff. "Earliest Uses of Some Words of Mathematics".
|
||||
Gladwell, Malcolm (19 December 2006). "In the Air: Who says big ideas are rare?". The New Yorker. Retrieved 15 January 2026. Stigler's law is described near the end of the article
|
||||
45
data/en.wikipedia.org/wiki/Strong_inference-0.md
Normal file
45
data/en.wikipedia.org/wiki/Strong_inference-0.md
Normal file
@ -0,0 +1,45 @@
|
||||
---
|
||||
title: "Strong inference"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Strong_inference"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T03:18:00.515209+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
In philosophy of science, strong inference is a model of scientific inquiry that emphasizes the need for alternative hypotheses, rather than a single hypothesis to avoid confirmation bias.
|
||||
The term "strong inference" was coined by John R. Platt, a biophysicist at the University of Chicago. Platt notes that some fields, such as molecular biology and high-energy physics, seem to adhere strongly to strong inference, with very beneficial results for the rate of progress in those fields.
|
||||
|
||||
|
||||
== The single hypothesis problem ==
|
||||
The problem with single hypotheses, confirmation bias, was aptly described by Thomas Chrowder Chamberlin in 1897:
|
||||
|
||||
The moment one has offered an original explanation for a phenomenon which seems satisfactory, that moment affection for [one’s] intellectual child springs into existence, and as the explanation grows into a definite theory [one’s] parental affections cluster about [the] offspring and it grows more and more dear .... There springs up also unwittingly a pressing of the theory to make it fit the facts and a pressing of the facts to make them fit the theory...
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The temptation to misinterpret results that contradict the desired hypothesis is probably irresistible.
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Despite the admonitions of Platt, reviewers of grant-applications often require "A Hypothesis" as part of the proposal (note the singular). Peer-review of research can help avoid the mistakes of single-hypotheses, but only so long as the reviewers are not in the thrall of the same hypothesis. If there is a shared enthrallment among the reviewers in a commonly believed hypothesis, then innovation becomes difficult because alternative hypotheses are not seriously considered, and sometimes not even permitted.
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== Strong Inference ==
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The method, very similar to the scientific method, is described as:
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Devising alternative hypotheses;
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Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses;
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Carrying out the experiment(s) so as to get a clean result;
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Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.
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The methods of Grey system theory effectively entertain strong inference. In such methods, the first step is the nullification of the single hypothesis by assuming that the true information of the system under study is only partially known.
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== Criticisms ==
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The original paper outlining strong inference has been criticized, particularly for overstating the degree that certain fields used this method.
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== Strong inference plus ==
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The limitations of Strong-Inference can be corrected by having two preceding phases:
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An exploratory phase: at this point information is inadequate so observations are chosen randomly or intuitively or based on scientific creativity.
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A pilot phase: in this phase statistical power is determined by replicating experiments under identical experimental conditions.
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These phases create the critical seed observation (s) upon which one can base alternative hypotheses.
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== References ==
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||||
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Reference in New Issue
Block a user