---
title: "Decline effect"
chunk: 1/3
source: "https://en.wikipedia.org/wiki/Decline_effect"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:36:35.033819+00:00"
instance: "kb-cron"
---

The decline effect may occur when scientific claims receive decreasing support over time. The term was first described by parapsychologist Joseph Banks Rhine in the 1930s to describe the disappearance of extrasensory perception (ESP) psychic experiments conducted by Rhine over time. More generally, Cronbach, in his review article "Beyond the two disciplines of scientific psychology" referred to the phenomenon as "generalizations decay." The term was once again used in a 2010 article by Jonah Lehrer published in The New Yorker.

== Examples ==
In his article, Lehrer gives several examples where the decline effect is allegedly showing. In the first example, the development of second generation anti-psychotic drugs, reveals that the first tests had demonstrated a dramatic decrease in the subjects' psychiatric symptoms. However, after repeating tests this effect declined and in the end it was not possible to document that these drugs had any better effect than the first generation anti-psychotics.
A well-known example of the decline effect can be seen in early experiments conducted by Professor Jonathan Schooler examining the effects of verbalization on non-verbal cognition. In an initial series of studies Schooler found evidence that verbal rehearsal of previously seen faces or colors markedly impaired subsequent recognition. This phenomenon is referred to as verbal overshadowing. Although verbal overshadowing effects have been repeatedly observed by Schooler, as well as other researchers, they have also proven to be somewhat challenging to replicate. Verbal overshadowing effects in a variety of domains were initially easy to find, but then became increasingly difficult to replicate indicating a decline effect in the phenomenon. Schooler has now become one of the more prominent researchers examining the decline effect. He has argued that addressing the decline effect may require a major revision to the scientific process whereby scientists log their protocols before conducting their research and then, regardless of outcome, report their findings in an open access repository (such as Brian Nosek's "Project Implicit"). Schooler is currently working with the Fetzer Foundation to organize a major meeting of scientists from various disciplines to consider alternative accounts of the decline effect and approaches for rigorously addressing it.
In 1991, Danish zoologist Anders Møller discovered a connection between symmetry and sexual preference of female birds in nature. This sparked a huge interest in the topic and a lot of follow-up research was published. In three years following the original discovery, 90% of studies confirmed Møller's hypothesis. However, the same outcome was published in just four out of eight research papers in 1995, and only a third in next three years.

A study published in 2022 reported perhaps one of the most striking examples of the decline effect in the field of ecology, where effect sizes of published studies testing for ocean acidification effects on fish behavior have declined by an order of magnitude over a decade of research on this topic.

== Explanations ==
The decline effect has different types, each with different causes.

=== False positive ===
If the initial publication is a false positive, i.e. the null hypothesis is true, but the initial publication mistakenly rejected it, then subsequent attempts at replication would necessarily discover that the effect size is not significantly different from zero. This is the simplest type of decline effect.
For example, statistically significant phenomena in parapsychology are false positives, and so is facilitated communication. The estimated effects of these phenomena become closer to zero with more experimental data, giving a decline effect.

=== Under-specification ===
If the initial publication discovered a genuine effect, but did not identify certain relevant variables, then the effect size might be smaller.
Concretely, consider this example. The effect 
  
    
      
        Y
      
    
    {\displaystyle Y}
  
 depends on 
  
    
      
        X
        ,
        Z
      
    
    {\displaystyle X,Z}
  
 according to 
  
    
      
        Y
        =
        X
        +
        Z
        +
        ϵ
      
    
    {\displaystyle Y=X+Z+\epsilon }
  
 where 
  
    
      
        ϵ
        ∼
        
          
            N
          
        
        (
        0
        ,
        1
        )
      
    
    {\displaystyle \epsilon \sim {\mathcal {N}}(0,1)}
  
 is a standard gaussian noise. Suppose in the initial publication, due to the experiment setup, 
  
    
      
        Z
        =
        X
      
    
    {\displaystyle Z=X}
  
, so the initial publication mistakenly thought that 
  
    
      
        Y
        =
        2
        X
        +
        ϵ
      
    
    {\displaystyle Y=2X+\epsilon }
  
.
In an attempt at replication, the uncontrolled variable 
  
    
      
        Z
      
    
    {\displaystyle Z}
  
 no longer correlates with 
  
    
      
        X
      
    
    {\displaystyle X}
  
, but varies independently according to 
  
    
      
        Z
        ∼
        
          
            N
          
        
        (
        0
        ,
        1
        )
      
    
    {\displaystyle Z\sim {\mathcal {N}}(0,1)}
  
. Now, the replication discovers that 
  
    
      
        Y
        =
        X
        +
        
          ϵ
          ′
        
      
    
    {\displaystyle Y=X+\epsilon '}
  
 where 
  
    
      
        
          ϵ
          ′
        
        ∼
        
          
            N
          
        
        (
        0
        ,
        2
        )
      
    
    {\displaystyle \epsilon '\sim {\mathcal {N}}(0,2)}
  
. Thus, the regression coefficient of 
  
    
      
        Y
      
    
    {\displaystyle Y}
  
 over 
  
    
      
        X
      
    
    {\displaystyle X}
  
 declined 50%.
A real example is the drug Timolol for treating glaucoma. Its effect has steadily decreased. This was explained by noting that the early studies used patients with advanced glaucoma, while later studies used less advanced patients. Because less sick patients has less room for improvement, the effect size of Timolol decreased.

=== Regression to the mean ===
One of the explanations of the effect is regression toward the mean, also known as "inflated decline". This is a statistical phenomenon happening when a variable is extreme on the first experiments and by later experiments tend to regress towards average, although this does not explain why sequential results decline in a linear fashion, rather than fluctuating about the true mean as would be expected.
This is particularly likely when the initial study was stopped early when "the effect size is clearly large enough". If one stops the data collection as soon as the effect size is above a threshold that is higher than the true effect size, then subsequent replications will necessarily regress to the mean.

=== Underpowered studies ===