---
title: "Replication crisis"
chunk: 5/15
source: "https://en.wikipedia.org/wiki/Replication_crisis"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:45:08.741659+00:00"
instance: "kb-cron"
---

=== Across fields ===
A 2016 survey by Nature on 1,576 researchers who took a brief online questionnaire on reproducibility found that more than 70% of researchers have tried and failed to reproduce another scientist's experiment results (including 87% of chemists, 77% of biologists, 69% of physicists and engineers, 67% of medical researchers, 64% of earth and environmental scientists, and 62% of all others), and more than half have failed to reproduce their own experiments. But fewer than 20% had been contacted by another researcher unable to reproduce their work. The survey found that fewer than 31% of researchers believe that failure to reproduce results means that the original result is probably wrong, although 52% agree that a significant replication crisis exists. Most researchers said they still trust the published literature. In 2010, Fanelli (2010) found that 91.5% of psychiatry/psychology studies confirmed the effects they were looking for, and concluded that the odds of this happening (a positive result) was around five times higher than in fields such as astronomy or geosciences. Fanelli argued that this is because researchers in "softer" sciences have fewer constraints to their conscious and unconscious biases.
Early analysis of result-blind peer review, which is less affected by publication bias, has estimated that 61% of result-blind studies in biomedicine and psychology have led to null results, in contrast to an estimated 5% to 20% in earlier research.
In 2021, a study conducted by University of California, San Diego found that papers that cannot be replicated are more likely to be cited. Nonreplicable publications are often cited more even after a replication study is published.

== Causes ==
There are many proposed causes for the replication crisis.

=== Historical and sociological causes ===
The replication crisis may be triggered by the "generation of new data and scientific publications at an unprecedented rate" that leads to "desperation to publish or perish" and failure to adhere to good scientific practice.
Predictions of an impending crisis in the quality-control mechanism of science can be traced back several decades. Derek de Solla Price—considered the father of scientometrics, the quantitative study of science—predicted in his 1963 book Little Science, Big Science that science could reach "senility" as a result of its own exponential growth. Some present-day literature seems to vindicate this "overflow" prophecy, lamenting the decay in both attention and quality.
Historian Philip Mirowski argues that the decline of scientific quality can be connected to its commodification, especially spurred by major corporations' profit-driven decision to outsource their research to universities and contract research organizations.
Social systems theory, as expounded in the work of German sociologist Niklas Luhmann, inspires a similar diagnosis. This theory holds that each system, such as economy, science, religion, and media, communicates using its own code: true and false for science, profit and loss for the economy, news and no-news for the media, and so on. According to some sociologists, science's mediatization, commodification, and politicization, as a result of the structural coupling among systems, have led to a confusion of the original system codes.

=== Problems with the publication system in science ===

==== Publication bias ====
Publication bias—the tendency to publish only positive, significant results—creates the "file drawer effect", where negative results remain unpublished. This produces misleading literature and biased meta-analyses. Only a very small proportion of academic journals in psychology and neurosciences explicitly welcomed submissions of replication studies in their aim and scope or instructions to authors. This does not encourage reporting on, or even attempts to perform, replication studies. Among 1,576 researchers Nature surveyed in 2016, only a minority had ever attempted to publish a replication, and several respondents who had published failed replications noted that editors and reviewers demanded that they play down comparisons with the original studies. An analysis of 4,270 empirical studies in 18 business journals from 1970 to 1991 reported that less than 10% of accounting, economics, and finance articles and 5% of management and marketing articles were replication studies. Publication bias is augmented by the pressure to publish and the author's own confirmation bias, and is an inherent hazard in the field, requiring a certain degree of skepticism on the part of readers.
When publication bias is considered along with the fact that a majority of tested hypotheses might be false a priori, it is plausible that a considerable proportion of research findings might be false positives, as shown by metascientist John Ioannidis. In turn, a high proportion of false positives in the published literature can explain why many findings are nonreproducible.
Another publication bias is that studies that do not reject the null hypothesis are scrutinized asymmetrically. For example, they are likely to be rejected as being difficult to interpret or having a Type II error. Studies that do reject the null hypothesis are not likely to be rejected for those reasons.
In popular media, there is another element of publication bias: the desire to make research accessible to the public led to oversimplification and exaggeration of findings, creating unrealistic expectations and amplifying the impact of non-replications. In contrast, null results and failures to replicate tend to go unreported. This explanation may apply to power posing's replication crisis.

==== Mathematical errors ====
Even high-impact journals have a significant fraction of mathematical errors in their use of statistics. For example, 11% of statistical results published in Nature and BMJ in 2001 are "incongruent", meaning that the reported p-value is mathematically different from what it should be if it were correctly calculated from the reported test statistic. These errors were likely from typesetting, rounding, and transcription errors.
Among 157 neuroscience papers published in five top-ranking journals that attempt to show that two experimental effects are different, 78 erroneously tested instead for whether one effect is significant while the other is not, and 79 correctly tested for whether their difference is significantly different from 0.

==== "Publish or perish" culture ====