Dr. R’s Blog about Replicability


For generalization, psychologists must finally rely, as has been done in all the older sciences, on replication (Cohen, 1994).


DEFINITION OF REPLICABILITY:  In empirical studies with random error variance replicability refers to the probability of a study with a significant result to produce a significant result again in an exact replication study of the first study using the same sample size and significance criterion.


2017 Blog Posts:

(October, 24, 2017)
Preliminary 2017 Replicability Rankings of 104 Psychology Journals

(September 19, 2017)
Reexaming the experiment to replace p-values with the probability of replicating an effect

(September 4, 2017)
The Power of the Pen Paradigm: A Replicability Analysis

(August, 2, 2017)
What would Cohen say: A comment on p < .005 as the new criterion for significance

(April, 7, 2017)
Hidden Figures: Replication failures in the stereotype threat literature

(February, 2, 2017)
Reconstruction of a Train Wreck: How Priming Research Went off the Rails

REPLICABILITY REPORTS:  Examining the replicability of research topics

RR No1. (April 19, 2016)  Is ego-depletion a replicable effect? 
RR No2. (May 21, 2016) Do mating primes have replicable effects on behavior?
RR No3. (September 4, 2017) The power of the pen paradigm: A replicability analysis




1.  Preliminary 2017  Replicability Rankings of 104 Psychology Journals
Rankings of 104 Psychology Journals according to the average replicability of a published significant result. Also includes detailed analysis of time trends in replicability from 2010 to 2017, and a comparison of psychological disciplines (cognitive, clinical, social, developmental, biological).


2.  Z-Curve: Estimating replicability for sets of studies with heterogeneous power (e.g., Journals, Departments, Labs)
This post presented the first replicability ranking and explains the methodology that is used to estimate the typical power of a significant result published in a journal.  The post provides an explanation of the new method to estimate observed power based on the distribution of test statistics converted into absolute z-scores.  The method has been developed further to estimate power for a wider range of z-scores by developing a model that allows for heterogeneity in power across tests.  A description of the new method will be published when extensive simulation studies are completed.


3. An Introduction to the R-Index
The R-Index can be used to predict whether a set of published results will replicate in a set of exact replication studies. It combines information about the observed power of the original studies with information about the amount of inflation in observed power due to publication bias (R-Index = Observed Median Power – Inflation). The R-Index has predicted the outcome of actual replication studies.

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4.  The Test of Insufficient Variance (TIVA)
The Test of Insufficient Variance is the most powerful test of publication bias and/or dishonest reporting practices. It can be used even if only two independent statistical results are available, although power to detect bias increases with the number of studies. After converting test results into z-scores, z-scores are expected to have a variance of one.   Unless power is very high, some of these z-scores will not be statistically significant (z .05 two-tailed).  If these non-significant results are missing, the variance shrinks, and TIVA detects that the variance is insufficient.  The observed variance is compared against the expected variance of 1 with a left-tailed chi-square test. The usefulness of TIVA is illustrated with Bem’s (2011) “Feeling the Future” data.

train-wreck-15.  MOST VIEWED POST (with comment by Noble Laureate Daniel Kahneman)
Reconstruction of a Train Wreck: How Priming Research Went off the Rails
This blog post examines the replicability of priming studies cited in Daniel Kahneman’s popular book “Thinking fast and slow.”   The results suggest that many of the cited findings are difficult to replicate.

http://schoolsnapshots.org/blog/2014/09/30/math-prize-for-girls-at-m-i-t/6. How robust are Stereotype-Threat Effects on Women’s Math Performance?
Stereotype-threat has been used by social psychologists to explain gender differences in math performance. Accordingly, the stereotype that men are better at math than women is threatening to women and threat leads to lower performance.  This theory has produced a large number of studies, but a recent meta-analysis showed that the literature suffers from publication bias and dishonest reporting.  After correcting for these effects, the stereotype-threat effect was negligible.  This blog post shows a low R-Index for the first article that appeared to provide strong support for stereotype-threat.  These results show that the R-Index can warn readers and researchers that reported results are too good to be true.

GPower7.  An attempt at explaining null-hypothesis testing and statistical power with 1 figure and 1500 words.   Null-hypothesis significance testing is old, widely used, and confusing. Many false claims have been used to suggest that NHST is a flawed statistical method. Others argue that the method is fine, but often misunderstood. Here I try to explain NHST and why it is important to consider power (type-II errors) using a picture from the free software GPower.


8.  The Problem with Bayesian Null-Hypothesis Testing
Some Bayesian statisticians have proposed Bayes-Factors to provide evidence for a Null-Hypothesis (i.e., there is no effect).  They used Bem’s (2011) “Feeling the Future” data to argue that Bayes-Factors would have demonstrated that extra-sensory perception does not exist.  This blog post shows that Bayes-Factors depend on the specification of the alternative hypothesis and that support for the null-hypothesis is often obtained by choosing an unrealistic alternative hypothesis (e.g., there is a 25% probability that effect size is greater than one standard deviation, d > 1).  As a result, Bayes-Factors can favor the null-hypothesis when there is an effect, but the effect size is small (d = .2).  A Bayes-Factor in favor of the null is more appropriately interpreted as evidence that the alternative hypothesis needs to decrease the probabilities assigned to large effect sizes. The post also shows that Bayes-Factors based on a meta-analysis of Bem’s data provide misleading evidence that an effect is present because Bayesian statistics do not take publication bias and dishonest reporting practices into account.

hidden9. Hidden figures: Replication failures in the stereotype threat literature.  A widespread problem is that failed replication studies are often not published. This blog post shows that another problem is that failed replication studies are ignored even when they are published.  Selective publishing of confirmatory results undermines the credibility of science and claims about the importance of stereotype threat to explain gender differences in mathematics.

20170620_14554410. My journey towards estimation of replicability.  In this blog post I explain how I got interested in statistical power and replicability and how I developed statistical methods to reveal selection bias and to estimate replicability.


(Preprint) Z-Curve: A Method for Estimating Replicability Based on Test Statistics in Original Studies (Schimmack & Brunner, 2017)

In this PDF document, Jerry Brunner and I would like to share our latest manuscript on z-curve,  a method that estimates average power of a set of studies selected for significance.  We call this estimate replicabilty because average power determines the success rate if the set of original studies were replicated exactly.

We welcome all comments and criticism as we plan to submit this manuscript to a peer-reviewed journal by December 1.


Comparison of P-curve and Z-Curve in Simulation studies

Estimate of average replicability in Cuddy et al.’s (2017) P-curve analysis of power posing with z-curve (30% for z-curve vs. 44% for p-curvce).

Estimating average replicability in psychology based on over 500,000 significant test statitics.

Comparing automated extraction of test statistics and focal hypothesis tests using Motyl et al.’s (2016) replicability analysis of social psychology.



Preliminary 2017 Replicability Rankings of 104 Psychology Journals

The table shows the preliminary 2017 rankings of 104 psychology journals.  A description of the methodology and analyses of by discipline and time are reported below the table.

Rank   Journal 2017 2016 2015 2014 2013 2012 2011 2010
1 European Journal of Developmental Psychology 93 88 67 83 74 71 79 65
2 Journal of Nonverbal Behavior 93 72 66 74 81 73 64 70
3 Behavioral Neuroscience 86 67 71 70 69 71 68 73
4 Sex Roles 83 83 75 71 73 78 77 74
5 Epilepsy & Behavior 82 82 82 85 85 81 87 77
6 Journal of Anxiety Disorders 82 77 73 77 76 80 75 77
7 Attention, Perception and Psychophysics 81 71 73 77 78 80 75 73
8 Cognitive Development 81 73 82 73 69 73 67 65
9 Judgment and Decision Making 81 79 78 78 67 75 70 74
10 Psychology of Music 81 80 72 73 77 72 81 86
11 Animal Behavior 80 74 71 72 72 71 70 78
12 Early Human Development 80 92 86 83 79 70 64 81
13 Journal of Experimental Psychology – Learning, Memory & Cognition 80 80 79 80 77 77 71 81
14 Journal of Memory and Language 80 84 81 74 77 73 80 76
15 Memory and Cognition 80 75 79 76 77 78 76 76
16 Social Psychological and Personality Science 80 67 61 65 61 58 63 55
17 Journal of Positive Psychology 80 70 72 72 64 64 73 81
18 Archives of Sexual Behavior 79 79 81 80 83 79 78 87
19 Consciousness and Cognition 79 71 69 73 67 70 73 74
20 Journal of Applied Psychology 79 80 74 76 69 74 72 73
21 Journal of Experimental Psychology – Applied 79 67 68 75 68 74 74 72
22 Journal of Experimental Psychology – General 79 75 73 73 76 69 74 69
23 Journal of Experimental Psychology – Human Perception and Performance 79 78 76 77 76 78 78 75
24 Journal of Personality 79 75 72 68 72 75 73 82
25 JPSP-Attitudes & Social Cognition 79 57 75 69 50 62 61 61
26 Personality and Individual Differences 79 79 79 78 78 76 74 73
27 Social Development 79 78 66 75 73 72 73 75
28 Appetite 78 74 69 66 75 72 74 77
29 Cognitive Behavioral Therapy 78 82 76 65 72 82 71 62
30 Journal of Comparative Psychology 78 77 76 83 83 75 69 64
31 Journal of Consulting and Clinical Psychology 78 71 68 65 66 66 69 68
32 Neurobiology of Learning and Memory 78 72 75 72 71 70 75 73
33 Psychonomic Bulletin and Review 78 79 82 79 82 72 71 78
34 Acta Psychologica 78 75 73 78 76 75 77 75
35 Behavior Therapy 77 74 71 75 76 78 64 76
36 Journal of Affective Disorders 77 85 84 77 83 82 76 76
37 Journal of Child and Family Studies 77 76 69 71 76 71 76 77
38 Journal of Vocational Behavior 77 85 84 69 82 79 86 74
39 Motivation and Emotion 77 64 67 66 67 65 79 68
40 Psychology and Aging 77 79 78 80 74 78 78 74
41 Psychophysiology 77 77 70 69 68 70 80 78
42 Britsh Journal of Social Psychology 76 65 66 62 64 60 72 63
43 Cognition 76 74 75 75 77 76 73 73
44 Cognitive Psychology 76 80 74 76 79 72 82 75
45 Developmental Psychology 76 77 77 75 71 68 70 70
46 Emotion 76 72 69 69 72 70 70 73
47 Frontiers in Behavioral Neuroscience 76 70 71 68 71 72 73 70
48 Frontiers in Psychology 76 75 73 73 72 72 70 82
49 Journal of Autism and Developmental Disorders 76 77 73 67 73 70 70 72
50 Journal of Social and Personal Relationships 76 82 60 63 69 67 79 83
51 Journal of Youth and Adolescence 76 88 81 82 79 76 79 74
52 Cognitive Therapy and Research 75 71 72 62 77 75 70 66
53 Depression & Anxiety 75 78 73 76 82 79 82 84
54 Journal of Child Psychology and Psychiatry and Allied Disciplines 75 63 66 66 72 76 58 66
55 Journal of Occupational and Organizational Psychology 75 85 84 71 77 77 74 67
56 Journal of Social Psychology 75 75 74 67 65 80 71 75
57 Political Psychology 75 81 75 72 75 74 51 70
58 Social Cognition 75 68 68 73 62 78 71 60
59 British Journal of Developmental Psychology 74 77 74 63 61 85 77 79
60 Evolution & Human Behavior 74 81 75 79 67 77 78 68
61 Journal of Research in Personality 74 77 82 80 79 73 74 71
62 Memory 74 79 66 83 73 71 76 78
63 Psychological Medicine 74 83 71 79 79 68 79 75
64 Psychopharmacology 74 75 73 73 71 73 73 71
65 Psychological Science 74 69 70 64 65 64 62 63
66 Behavioural Brain Research 73 69 75 69 71 72 73 74
67 Behaviour Research and Therapy 73 74 76 77 74 77 68 71
68 Journal of Cross-Cultural Psychology 73 75 80 78 78 71 76 76
69 Journal of Experimental Child Psychology 73 73 78 74 74 72 72 76
70 Personality and Social Psychology Bulletin 73 71 65 65 61 61 62 61
71 Social Psychology 73 75 72 74 69 64 75 74
72 Developmental Science 72 68 68 66 71 68 68 66
73 Journal of Cognition and Development 72 78 68 64 69 62 66 70
74 Law and Human Behavior 72 76 76 61 76 76 84 72
75 Perception 72 78 79 74 78 85 94 91
76 Journal of Applied Social Psychology 71 81 69 72 71 80 74 75
77 Journal of Experimental Social Psychology 71 68 63 61 58 56 58 57
78 Annals of Behavioral Medicine 70 70 62 71 71 77 75 71
79 Frontiers in Human Neuroscience 70 74 73 74 75 75 75 72
80 Health Psychology 70 63 68 69 68 63 70 72
81 Journal of Abnormal Child Psychology 70 74 70 74 78 78 68 78
82 Journal of Counseling Psychology 70 69 74 75 76 78 67 80
83 Journal of Educational Psychology 70 74 73 76 76 78 78 84
84 Journal of Family Psychology 70 68 75 71 73 66 68 69
85 JPSP-Interpersonal Relationships and Group Processes 70 74 64 62 66 58 60 56
86 Child Development 69 72 72 71 69 75 72 75
87 European Journal of Social Psychology 69 76 64 72 67 59 69 66
88 Group Processes & Intergroup Relations 69 67 73 68 70 66 68 61
89 Organizational Behavior and Human Decision Processes 69 73 70 70 72 70 71 65
90 Personal Relationships 69 72 71 70 68 74 60 69
91 Journal of Pain 69 79 71 81 73 78 74 72
92 Journal of Research on Adolescence 68 78 69 68 75 76 84 77
93 Self and Identity 66 70 56 73 71 72 70 73
94 Developmental Psychobiology 65 69 67 69 70 69 71 66
95 Infancy 65 61 57 65 70 67 73 57
96 Hormones & Behavior 64 68 66 66 67 64 68 67
97 Journal of Abnormal Psychology 64 67 71 64 71 67 73 70
98 JPSP-Personality Processes and Individual Differences 64 74 70 70 72 71 71 64
99 Psychoneuroendocrinology 64 68 66 65 65 62 66 63
100 Cognition and Emotion 63 69 75 72 76 76 76 76
101 European Journal of Personality 62 78 66 81 70 74 74 78
102 Biological Psychology 61 68 70 66 65 62 70 70
103 Journal of Happiness Studies 60 78 79 72 81 78 80 83
104 Journal of Consumer Psychology 58 56 69 66 61 62 61 66



Download PDF of this ggplot representation of the table courtesy of David Lovis-McMahon.







I define replicability as the probability of obtaining a significant result in an exact replication of a study that produced a significant result.  In the past five years, there have been concerns about a replication crisis in psychology.  Even results that are replicated internally by the same author multiple times fail to replicate in independent replication attempts (Bem, 2011).  The key reason for the replication crisis is selective publishing of significant results (publication bias). While journals report over 95% significant results (Sterling, 1959; Sterling et al., 1995), a 2015 article estimated that less than 50% of these results can be replicated  (OSC, 2015).

The OSC reproducibility made an important contribution by demonstrating that published results in psychology have low replicability.  However, the reliance on actual replication studies has a a number of limitations.  First, actual replication studies are expensive, time-consuming, and sometimes impossible (e.g., a longitudinal study spanning 20 years).  This makes it difficult to rely on actual replication studies to assess the replicability of psychological results, produce replicability rankings of journals, and to track replicability over time.

Schimmack and Brunner (2016) developed a statistical method (z-curve) that makes it possible to estimate average replicability for a set of published results based on the test-statistics reported in published articles.  This statistical approach to the estimation of replicability has several advantages over the use of actual replication studies: (a) replicability can be assessed in real time, (b) it can be estimated for all published results rather than a small sample of studies, and (c) it can be applied to studies that are impossible to reproduce.  Finally, it has the advantage that actual replication studies can be criticized  (Gilbert, King, Pettigrew, & Wilson, 2016). Estimates of replicabilty based on original studies do not have this problem because they are based on results reported in the original articles.

Z-curve has been validated with simulation studies and can be used with heterogeneous sets of studies that vary across statistical methods, sample sizes, and effect sizes  (Brunner & Schimmack, 2016).  I have applied this method to articles published in psychology journals to create replicability rankings of psychology journals in 2015 and 2016.  This blog post presents preliminary rankings for 2017 based on articles that have been published so far. The rankings will be updated in 2018, when all 2017 articles are available.

For the 2016 rankings, I used z-curve to obtain annual replicability estimates for 103 journals from 2010 to 2016.  Analyses of time trends showed no changes from 2010-2015. However, in 2016 there were first signs of an increase in replicabilty.  Additional analyses suggested that social psychology journals contributed mostly to this trend.  The preliminary 2017 rankings provide an opportunity to examine whether there is a reliable increase in replicability in psychology and whether such a trend is limited to social psychology.


Journals were mainly selected based on impact factor.  Preliminary replicability rankings for 2017 are based on 104 journals. Several new journals were added to increase the number of journals specializing in five disciplines: social (24), cognitive (13), development (15), clinical/medical (18), biological (13).  The other 24 journals were broad journals (Psychological Science) or from other disciplines.  The total number of journals for the preliminary rankings is 104.  More journals will be added to the final rankings for 2017.

Data Preparation

All PDF versions of published articles were downloaded and converted into text files using the conversion program pdfzilla.  Text files were searched for reports of statistical results using a self-created R program. Only F-tests, t-tests, and z-tests were used for the rankings because they can be reliabilty extracted from diverse journals. t-values that were reported without df were treated as z-values which leads to a slight inflation in replicability estimates. However, the bulk of test-statistics were F-values and t-values with degrees of freedom. Test-statistics were converted into exact p-values and exact p-values were converted into absolute z-scores as a measure of the strength of evidence against the null-hypothesis.

Data Analysis

The data for each year were analyzed using z-curve (Schimmack and Brunner (2016). Z-curve provides a replicability estimate. In addition, it generates a Powergraph. A Powergraph is essentially a histogram of absolute z-scores. Visual inspection of Powergraphs can be used to examine publication bias. A drop of z-values on the left side of the significance criterion (p < .05, two-tailed, z = 1.96) shows that non-significant results are underpresented. A further drop may be visible at z = 1.65 because values between z = 1.65 and z = 1.96 are sometimes reported as marginally significant support for a hypothesis.  The critical values z = 1.65 and z = 1.96 are marked by vertical red lines in the Powergraphs.

Replicabilty rankings rely only on statistically significant results (z > 1.96).  The aim of z-curve is to estimate the average probability that an exact replication of a study that produced a significant result produces a significant result again.  As replicability estimates rely only on significant results, journals are not being punished for publishing non-significant results.  The key criterion is how strong the evidence against the null-hypothesis is when an article published results that lead to the rejection of the null-hypothesis.

Statistically, replicability is the average statistical power of the set of studies that produced significant results.  As power is the probabilty of obtaining a significant result, average power of the original studies is equivalent with average power of a set of exact replication studies. Thus, average power of the original studies is an estimate of replicability.

Links to powergraphs for all journals and years are provided in the ranking table.  These powergraphs provide additional information that is not used for the rankings. The only information that is being used is the replicability estimate based on the distribution of significant z-scores.


The replicability estimates for each journal and year (104 * 8 = 832 data points) served as the raw data for the following statistical analyses.  I fitted a growth model to examine time trends and variability across journals and disciplines using MPLUS7.4.

I compared several models. Model 1 assumed no mean level changes and stable variability across journals (significant variance in the intercept/trait). Model 2 assumed no change from 2010 to 2015 and allowed for mean level changes in 2016 and 2017 as well as stable differences between journals. Model 3 was identical to Model 2 and allowed for random variability in the slope factor.

Model 1 did not have acceptable fit (RMSEA = .109, BIC = 5198). Model 2 increased fit (RMSEA = 0.063, BIC = 5176).  Model 3 did not improve model fit (RMSEA = .063, BIC = 5180), the variance of the slope factor was not significant, and BIC favored the more parsimonious Model 2.  The parameter estimates suggested that replicability estimates increased from 72 in the years from 2010 to 2015 by 2 points to 74 (z = 3.70, p < .001).

The standardized loadings of individual years on the latent intercept factor ranged from .57 to .61.  This implies that about one-third of the variance is stable, while the remaining two-thirds of the variance is due to fluctuations in estimates from year to year.

The average of 72% replicability is notably higher than the estimate of 62% reported in the 2016 rankings.  The difference is due to a computational error in the 2016 rankings that affected mainly the absolute values, but not the relative ranking of journals. The r-code for the 2016 rankings miscalculated the percentage of extreme z-scores (z > 6), which is used to adjust the z-curve estimate that are based on z-scores between 1.96 and 6 because all z-scores greater than 6 essentially have 100% power.  For the 2016 rankings, I erroneously computed the percentage of extreme z-scores out of all z-scores rather than limiting it to the set of statistically significant results. This error became apparent during new simulation studies that produced wrong estimates.

Although the previous analysis failed to find significant variability for the slope (change factor), this could be due to the low power of this statistical test.  The next models included disciplines as predictors of the intercept (Model 4) or the intercept and slope (Model 5).  Model 4 had acceptable fit (RMSEA = .059, BIC = 5175), but Model 5 improved fit, although BIC favored the more parsimonious model (RMSEA = .036, BIC = 5178). The Bayesian Information Criterion favors parsimony and better fit cannot be interpreted as evidence for the absence of an effect.  Model 5 showed two significant (p < .05) effects for social and developmental psychology.  In Model 6 I included only social and development as predictors of the slope factor.  BIC favored this model over the other models (RMSEA = .029, BIC = 5164).  The model results showed improvements for social psychology (increase by 4.48 percentage points, z = 3.46, p = .001) and developmental psychology (increase by 3.25 percentage points, z = 2.65, p = .008).  Whereas the improvement for social psychology was expected based on the 2016 results, the increase for developmental psychology was unexpected and requires replication in the 2018 rankings.

The only significant predictors for the intercept were social psychology (-4.92 percentage points, z = 4.12, p < .001) and cognitive psychology (+2.91, z = 2.15, p = .032).  The strong negative effect (standardized effect size d = 1.14) for social psychology confirms earlier findings that social psychology was most strongly affected by the replication crisis (OSC, 2015). It is encouraging to see that social psychology is also the discipline with the strongest evidence for improvement in response to the replication crisis.  With an increase by 4.48 points, replicabilty of social psychology is now at the same level as other disciplines in psychology other than cognitive psychology, which is still a bit more replicable than all other disciplines.

In conclusion, the results confirm that social psychology had lower replicability than other disciplines, but also shows that social psychology has significantly improved in replicabilty over the past couple of years.

Analysis of Individual Journals

The next analysis examined changes in replicabilty at the level of individual journals. Replicability estimates were regressed on a dummy variable that contrasted 2010-1015 (0) with 2016-2017 (1). This analysis produced 10 significant increases with p < .01 (one-tailed), when only 1 out of 100 would be expected by chance.

Five of the 10 journals (50% vs. 20% in the total set of journals) were from social psychology (SPPS + 13, JESP + 11, JPSP-IRGP + 11, PSPB + 10, Sex Roles + 8).  The remaining journals were from developmental psychology (European J. Dev. Psy + 17, J Cog. Dev. + 9), clinical psychology (J. Cons. & Clinical Psy + 8, J. Autism and Dev. Disorders + 6), and the Journal of Applied Psychology (+7).  The high proportion of social psychology journals provides further evidence that social psychology has responded most strongly to the replication crisis.



Although z-curve provides very good absolute estimates of replicability in simulation studies, the absolute values in the rankings have to be interpreted with a big grain of salt for several reasons.  Most important, the rankings are based on all test-statistics that were reported in an article.  Only a few of these statistics test theoretically important hypothesis. Others may be manipulation checks or other incidental analyses.  For the OSC (2015) studies the replicability etimate was 69% when the actual success rate was only 37%.  Moreover, comparisons of the automated extraction method used for the rankings and hand-coding of focal hypothesis in the same article also show a 20% point difference.  Thus, a posted replicability of 70% may imply only 50% replicability for a critical hypothesis test.  Second, the estimates are based on the ideal assumptions underlying statistical test distributions. Violations of these assumptions (outliers) are likely to reduce actual replicability.  Third, actual replication studies are never exact replication studies and minor differences between the studies are also likely to reduce replicability.  There are currently not sufficient actual replication studies to correct for these factors, but the average is likely to be less than 72%. It is also likely to be higher than 37% because this estimate is heavily influenced by social psychology, while cognitive psychology had a success rate of 50%.  Thus, a plausible range of the typical replicability of psychology is somwhere between 40% and 60%.  We might say the glass is half full and have empty, while there is systematic variation around this average across journals.


55 years after Cohen (1962) pointed out that psychologists conduct many studies that produce non-significant results (type-II errors).  For decades there was no sign of improvement.  The preliminary rankings of 2017 provide the first empirical evidence that psychologists are waking up to the replication crisis caused by selective reporting of significant results from underpowered studies.  Right now, social psychologists appear to respond most strongly to concerns about replicability.  However, it is possible that other disciplines will follow in the future as the open science movement is gaining momentum.  Hopefully, replicabilty rankings can provide an incentive to consider replicability as one of several criterion for publication.   A study with z = 2.20 and another study with z = 3.85 are both significant (z > 1.96), but a study with z =3.85 has a higher chance of being replicable. Everything else being equal, editors should favor studies with stronger evidence; that is higher z-scores (a.k.a, lower p-values).  By taking the strength of evidence into account, psychologists can move away from treating all significant results (p < .05) as equal and take type-II errors and power into account.


P-REP (2005-2009): Reexamining the experiment to replace p-values with the probability of replicating an effect

In 2005, Psychological Science published an article titled “An Alternative to Null-Hypothesis Significance Tests” by Peter R. Killeen.    The article proposed to replace p-values and significance testing with a new statistic; the probability of replicating an effect (P-rep).  The article generated a lot of excitement and for a period from 2006 to 2009, Psychological Science encouraged reporting p-rep.   After some statistical criticism and after a new editor took over Psychological Science, interest in p-rep declined (see Figure).

It is ironic that only a few years later, psychological science would encounter a replication crisis, where several famous experiments did not replicate.  Despite much discussion about replicability of psychological science in recent years, Killeen’s attempt to predict replication outcome has been hardly mentioned.  This blog post reexamines p-rep in the context of the current replication crisis.

The abstract clearly defines p-rep as an estimate of “the probability of replicating an effect” (p. 345), which is the core meaning of replicability. Factories have high replicability (6 sigma) and produce virtually identical products that work with high probability. However, in empirical research it is not so easy to define what it means to get the same result. So, the first step in estimating replicability is to define the result of a study that a replication study aims to replicate.

“Traditionally, replication has been viewed as a second successful attainment of a significant effect” (Killeen, 2005, p. 349). Viewed from this perspective, p-rep would estimate the probability of obtaining a significant result (p < alpha) after observing a significant result in an original study.

Killeen proposes to change the criterion to the sign of the observed effect size. This implies that p-rep can only be applied to hypothesis with a directional hypothesis (e.g, it does not apply to tests of explained variance).  The criterion for a successful replication then becomes observing an effect size with the same sign as the original study.

Although this may appear like a radical change from null-hypothesis significance testing, this is not the case.  We can translate the sign criterion into an alpha level of 50% in a one-tailed t-test.  For a one-tailed t-test, negative effect sizes have p-values ranging from 1 to .50 and positive effect sizes have p-values ranging from .50 to 0.  So, a successful outcome is associated with a p-value below .50 (p < .50).

If we observe a positive effect size in the original study, we can compute the power of obtaining a positive result in a replicating study with a post-hoc power analysis, where we enter information about the standardized effect size, sample size, and alpha = .50, one-tailed.

Using R syntax this can be achieved with the formula:


with obs.es being the observed standardized effect size (Cohen’s d), N = total sample size, and se = sampling error = 2/sqrt(N).

The similarity to p-rep is apparent, when we look at the formula for p-rep.


There are two differences. First, p-rep uses the standard normal distribution to estimate power. This is a simplification that ignores the degrees of freedom.  The more accurate formula for power is the non-central t-distribution that takes the degrees of freedom (N-2) into account.  However, even with modest sample sizes of N  =40, this simplification has negligible effects on power estimates.

The second difference is that p-rep reduces the non-centrality parameter (effect size/sampling error) by a factor of square-root 2.  Without going into the complex reasoning behind this adjustment, the end-result of the adjustment is that p-rep will be lower than the standard power estimate.

Using Killeen’s example on page 347 with d = .5 and N = 20, p-rep = .785.  In contrast, the power estimate with alpha = .50 is .861.

The comparison of p-rep with standard power analysis brings up an interesting and unexplored question. “Does p-rep really predict the probability of replication?”  (p. 348).  Killeen (2005) uses meta-analyses to answer this question.  In one example, he found that 70% of studies showed a negative relation between heart rate and aggressive behaviors.  The median value of p-rep over those studies was 71%.  Two other examples are provided.

A better way to evaluate estimates of replicability is to conduct simulation studies where the true answer is known.  For example, a simulation study can simulate 1,000,000 exact replications of Killeen’s example with d = .5 and N = 20 and we can observe how many studies show a positive observed effect size.  In a single run of this simulation, 86,842 studies showed a positive sign. Median P-rep (.788) underestimates this actual success rate, whereas median observed power more closely predicts the observed success rate (.861).

This is not surprising.  Power analysis is designed to predict the long-term success rate given a population effect size, a criterion value, and sampling error.  The adjustment made by Killeen is unnecessary and leads to the wrong prediction.

P-rep applied to Single Studies

It is also peculiar to use meta-analyses to test the performance of p-rep because a meta-analysis implies that many studies have been conducted, whereas the goal of p-rep was to predict the outcome of a single replication study from the outcome of an original study.

This primary aim also explains the adjustment to the non-centrality parameter, which was based on the idea to add the sampling variances of the original and replication study.  Finally, Killeen clearly states that the goal of p-rep is to ignore population effect sizes and to define replicability as “an effect of the same sign as that found in the original experiment” (p. 346).  This is very different from power analysis, which estimates the probability of an effect of the same sign as the population effect size.

We can evaluate p-rep as a predictor of obtaining effect sizes with the same direction in two studies with another simulation study.  Assume that the effect size is d = .20 and the total sample size is also small (N = 20).  The median p-rep estimate is 62%.

The 2 x 2 table shows how often the effect sizes of the original study and the replication study match.

Negative Positive
Negative 11% 22%
Positive 22% 45%

The table shows that the original and replication study match only 45% of the time when the sign also matches the population effect size. Another 11% matches occur when the original and the replication study show the wrong sign and future replication studies are more likely to show the opposite effect size.  Although these cases meet the definition of replicability with the sign of the original study as criterion, it seems questionable to define a pair of studies that both show the wrong result as a successful replication.  Furthermore, the median p-rep estimate of 62% is inconsistent with the correctly matched cases (45%) or the total number of matched cases (45% + 11% = 56%).  In conclusion, it is neither sensible to define replicability as consistency between pairs of exact replication studies, nor does p-rep estimate this probability very well.

Can we fix it?

The previous examination of p-rep showed that it is essentially an observed power estimate with alpha = 50% and an attenuated non-centrality parameter.  Does this mean we can fix p-rep and turn it into a meaningful statistic?  In other words, is it meaningful to compute the probability that future replication studies will reveal the direction of the population effect size by computing power with alpha = 50%?

For example, a research finds an effect size of d = .4 with a total sample size of N = 100.  Using a standard t-test, the research can report the traditional p-value; p = .048.

Negative Positive
Negative 0% 2%
Positive 2% 96%

The simulation results show that the most observations show consistent signs in pairs of studies and are also consistent with the population effect size.  Median observed power, the new p-rep, is 98%. So, is a high p-rep value a good indicator that future studies will also produce a positive sign?

The main problem with observed power analysis is that it relies on the observed effect size as an estimate of the population effect size.  However, in small samples, the difference between observed effect sizes and population effect sizes can be large, which leads to very variable estimates of p-rep. One way to alert readers to the variability in replicability estimates is to provide a confidence interval around the estimate.  As p-rep is a function of the observed effect size, this is easily achieved by converting the lower and upper limit of the confidence interval around the effect size into a confidence interval for p-rep.  With d = .4 and N = 100 (sampling error = 2/sqrt(100) = .20), the confidence interval of effect sizes ranges from d = .008 to d = .792.  The corresponding p-rep values are 52% to 100%.

Importantly, a value of 50% is the lower bound for p-rep and corresponds to determining the direction of the effect by a coin toss.  In other words, the point estimate of replicability can be highly misleading because the observed effect size may be considerably lower than the population effect size.   This means that reporting the point-estimate of p-rep can give false assurance about replicability, while the confidence interval shows that there is tremendous uncertainty around this estimate.

Understanding Replication Failures

Killeen (2005) pointed out that it can be difficult to understand replication failures using the traditional criterion of obtaining a significant result in the replication study.  For example, the original study may have reported a significant result with p = .04 and the replication study produced a non-significant p-value of p = .06.  According to the criterion of obtaining a significant result in the replication study, this outcome is a disappointing failure.  Of course, there is no meaningful difference between p = .04 and p = .06. It just so happens that they are on opposite sides of an arbitrary criterion value.

Killeen suggests that we can avoid this problem by reporting p-rep.  However, p-rep just changes the arbitrary criterion value from p = .05 to d = 0.  It is still possible that a replication study will fail because the effect sizes do not match.  Whereas the effect size in an original study was d = .05, the effect size in the replication study was d = -.05.  In small samples, this is not a meaningful difference in effect sizes, but the outcome constitutes a replication failure.

There is simply no way around making mistakes in inferential statistics.  We can only try to minimize them at the expense of reducing sampling error.  By setting alpha to 50%, we are reducing type-II errors (failing to support a correct hypothesis) at the expense of increasing the risk of a type-I error (failing to accept the wrong hypothesis), but errors will be made.

P-rep and Publication Bias

Killeen (2005) points out another limitation of p-rep.  “One might, of course, be misled by a value of prep that itself cannot be replicated. This can be caused by publication bias against small or negative effects.” (p. 350).  Here we see the real problem of raising alpha to 50%.  If there is no effect (d = 0), one out of two studies will produce a positive result that can be published.  If 100 researchers test an interesting hypothesis in their lab, but only positive results will be published, approximately 50 articles will support a false conclusion, while 50 other articles that showed the opposite result will be hidden in file drawers.  A stricter alpha criterion is needed to minimize the rate of false inferences, especially when publication bias is present.

A counter-argument could be that researchers who find a negative result can also publish their results, because positive and negative results are equally publishable. However, this would imply that journals are filled with inconsistent results and research areas with small effects and small samples will publish nearly equal number of studies with positive and negative results. Each article would draw a conclusion based on the results of a single study and try to explain inconsistent with potential moderator variables.  By imposing a stricter criterion for sufficient evidence, published results are more consistent and more likely to reflect a true finding.  This is especially true, if studies have sufficient power to reduce the risk of type-II errors and if journals do not selectively report studies with positive results.

Does this mean estimating replicability is a bad idea?

Although Killeen’s (2005) main goal was to predict the outcome of a single replication study, he did explore how well median replicability estimates predicted the outcome of meta-analysis.  As aggregation across studies reduces sampling error, replicability estimates based on sets of studies can be useful to predict actual success rates in studies (Sterling et al., 1995).  The comparison of median observed power with actual success rates can be used to reveal publication bias (Schimmack, 2012) and median observed power is a valid predictor of future study outcomes in the absence of publication bias and for homogeneous sets of studies. More advanced methods even make it possible to estimate replicability when publication bias is present and when the set of studies is heterogenous (Brunner & Schimmack, 2016).  So, while p-rep has a number of shortcomings, the idea of estimating replicability deserves further attention.


The rise and fall of p-rep in the first decade of the 2000s tells an interesting story about psychological science.  In hindsight, the popularity of p-rep is consistent with an area that focused more on discoveries than on error control.  Ideally, every study, no matter how small, would be sufficient to support inferences about human behavior.  The criterion to produce a p-value below .05 was deemed an “unfortunate historical commitment to significance testing” (p. 346), when psychologists were only interested in the direction of the observed effect size in their sample.  Apparently, there was no need to examine whether the observed effect size in a small sample was consistent with a population effect size or whether the sign would replicate in a series of studies.

Although p-rep never replaced p-values (most published p-rep values convert into p-values below .05), the general principles of significance testing were ignored. Instead of increasing alpha, researchers found ways to lower p-values to meet the alpha = .05 criterion. A decade later, the consequences of this attitude towards significance testing are apparent.  Many published findings do not hold up when they are subjected to an actual replication attempt by researchers who are willing to report successes and failures.

In this emerging new era, it is important to teach a new generation of psychologists how to navigate the inescapable problem of inferential statistics: you will make errors. Either you falsely claim a discovery of an effect or you fail to provide sufficient evidence for an effect that does exist.  Errors are part of science. How many and what type of errors will be made depends on how scientists conduct their studies.

What would Cohen say? A comment on p < .005

Most psychologists are trained in Fisherian statistics, which has become known as Null-Hypothesis Significance Testing (NHST).  NHST compares an observed effect size against a hypothetical effect size. The hypothetical effect size is typically zero; that is, the hypothesis is that there is no effect.  The deviation of the observed effect size from zero relative to the amount of sampling error provides a test statistic (test statistic = effect size / sampling error).  The test statistic can then be compared to a criterion value. The criterion value is typically chosen so that only 5% of test statistics would exceed the criterion value by chance alone.  If the test statistic exceeds this value, the null-hypothesis is rejected in favor of the inference that an effect greater than zero was present.

One major problem of NHST is that non-significant results are not considered.  To address this limitation, Neyman and Pearson extended Fisherian statistic and introduced the concepts of type-I (alpha) and type-II (beta) errors.  A type-I error occurs when researchers falsely reject a true null-hypothesis; that is, they infer from a significant result that an effect was present, when there is actually no effect.  The type-I error rate is fixed by the criterion for significance, which is typically p < .05.  This means, that a set of studies cannot produce more than 5% false-positive results.  The maximum of 5% false positive results would only be observed if all studies have no effect. In this case, we would expect 5% significant results and 95% non-significant results.

The important contribution by Neyman and Pearson was to consider the complementary type-II error.  A type-II error occurs when an effect is present, but a study produces a non-significant result.  In this case, researchers fail to detect a true effect.  The type-II error rate depends on the size of the effect and the amount of sampling error.  If effect sizes are small and sampling error is large, test statistics will often be too small to exceed the criterion value.

Neyman-Pearson statistics was popularized in psychology by Jacob Cohen.  In 1962, Cohen examined effect sizes and sample sizes (as a proxy for sampling error) in the Journal of Abnormal and Social Psychology and concluded that there is a high risk of type-II errors because sample sizes are too small to detect even moderate effect sizes and inadequate to detect small effect sizes.  Over the next decades, methodologists have repeatedly pointed out that psychologists often conduct studies with a high risk to fail; that is, to provide empirical evidence for real effects (Sedlemeier & Gigerenzer, 1989).

The concern about type-II errors has been largely ignored by empirical psychologists.  One possible reason is that journals had no problem filling volumes with significant results, while rejecting 80% of submissions that also presented significant results.  Apparently, type-II errors were much less common than methodologists feared.

However, in 2011 it became apparent that the high success rate in journals was illusory. Published results were not representative of studies that were conducted. Instead, researchers used questionable research practices or simply did not report studies with non-significant results.  In other words, the type-II error rate was as high as methodologists suspected, but selection of significant results created the impression that nearly all studies were successful in producing significant results.  The influential “False Positive Psychology” article suggested that it is very easy to produce significant results without an actual effect.  This led to the fear that many published results in psychology may be false positive results.

Doubt about the replicability and credibility of published results has led to numerous recommendations for the improvement of psychological science.  One of the most obvious recommendations is to ensure that published results are representative of the studies that are actually being conducted.  Given the high type-II error rates, this would mean that journals would be filled with many non-significant and inconclusive results.  This is not a very attractive solution because it is not clear what the scientific community can learn from an inconclusive result.  A better solution would be to increase the statistical power of studies. Statistical power is simply the inverse of a type-II error (power = 1 – beta).  As power increases, studies with a true effect have a higher chance of producing a true positive result (e.g., a drug is an effective treatment for a disease). Numerous articles have suggested that researchers should increase power to increase replicability and credibility of published results (e.g., Schimmack, 2012).

In a recent article, a team of 72 authors proposed another solution. They recommended that psychologists should reduce the probability of a type-I error from 5% (1 out of 20 studies) to 0.5% (1 out of 200 studies).  This recommendation is based on the belief that the replication crisis in psychology reflects a large number of type-I errors.  By reducing the alpha criterion, the rate of type-I errors will be reduced from a maximum of 10 out of 200 studies to 1 out of 200 studies.

I believe that this recommendation is misguided because it ignores the consequences of a more stringent significance criterion on type-II errors.  Keeping resources and sampling error constant, reducing the type-I error rate increases the type-II error rate. This is undesirable because the actual type-II error is already large.

For example, a between-subject comparison of two means with a standardized effect size of d = .4 and a sample size of N = 100 (n = 50 per cell) has a 50% risk of a type-II error.  The risk of a type-II error raises to 80%, if alpha is reduced to .005.  It makes no sense to conduct a study with an 80% chance of failure (Tversky & Kahneman, 1971).  Thus, the call for a lower alpha implies that researchers will have to invest more resources to discover true positive results.  Many researchers may simply lack the resources to meet this stringent significance criterion.

My suggestion is exactly opposite to the recommendation of a more stringent criterion.  The main problem for selection bias in journals is that even the existing criterion of p < .05 is too stringent and leads to a high percentage of type-II errors that cannot be published.  This has produced the replication crisis with large file-drawers of studies with p-values greater than .05,  the use of questionable research practices, and publications of inflated effect sizes that cannot be replicated.

To avoid this problem, researchers should use a significance criterion that balances the risk of a type-I and type-II error.  For example, with an expected effect size of d = .4 and N = 100, researchers should use p < .20 for significance, which reduces the risk of a type -II error to 20%.  In this case, type-I and type-II error are balanced.  If the study produces a p-value of, say, .15, researchers can publish the result with the conclusion that the study provided evidence for the effect. At the same time, readers are warned that they should not interpret this result as strong evidence for the effect because there is a 20% probability of a type-I error.

Given this positive result, researchers can then follow up their initial study with a larger replication study that allows for a stricter type-I error control, while holding power constant.   With d = 4, they now need N = 200 participants to have 80% power and alpha = .05.  Even if the second study does not produce a significant result (the probability that two studies with 80% power are significant is only 64%, Schimmack, 2012), researchers can combine the results of both studies and with N = 300, the combined studies have 80% power with alpha = .01.

The advantage of starting with smaller studies with a higher alpha criterion is that researchers are able to test risky hypothesis with a smaller amount of resources.  In the example, the first study used “only” 100 participants.  In contrast, the proposal to require p < .005 as evidence for an original, risky study implies that researchers need to invest a lot of resources in a risky study that may provide inconclusive results if it fails to produce a significant result.  A power analysis shows that a sample size of N = 338 participants is needed to have 80% power for an effect size of d = .4 and p < .005 as criterion for significance.

Rather than investing 300 participants into a risky study that may produce a non-significant and uninteresting result (eating green jelly beans does not cure cancer), researchers may be better able and willing to start with 100 participants and to follow up an encouraging result with a larger follow-up study.  The evidential value that arises from one study with 300 participants or two studies with 100 and 200 participants is the same, but requiring p < .005 from the start discourages risky studies and puts even more pressure on researchers to produce significant results if all of their resources are used for a single study.  In contrast, lowering alpha reduces the need for questionable research practices and reduces the risk of type-II errors.

In conclusion, it is time to learn Neyman-Pearson statistic and to remember Cohen’s important contribution that many studies in psychology are underpowered.  Low power produces inconclusive results that are not worthwhile publishing.  A study with low power is like a high-jumper that puts the bar too high and fails every time. We learned nothing about the jumpers’ ability. Scientists may learn from high-jump contests where jumpers start with lower and realistic heights and then raise the bar when they succeeded.  In the same manner, researchers should conduct pilot studies or risky exploratory studies with small samples and a high type-I error probability and lower the alpha criterion gradually if the results are encouraging, while maintaining a reasonably low type-II error.

Evidently, a significant result with alpha = .20 does not provide conclusive evidence for an effect.  However, the arbitrary p < .005 criterion also fails short of demonstrating conclusively that an effect exists.  Journals publish thousands of results a year and some of these results may be false positives, even if the error rate is set at 1 out of 200. Thus, p < .005 is neither defensible as a criterion for a first exploratory study, nor conclusive evidence for an effect.  A better criterion for conclusive evidence is that an effect can be replicated across different laboratories and a type-I error probability of less than 1 out of a billion (6 sigma).  This is by no means an unrealistic target.  To achieve this criterion with an effect size of d = .4, a sample size of N = 1,000 is needed.  The combined evidence of 5 labs with N = 200 per lab would be sufficient to produce conclusive evidence for an effect, but only if there is no selection bias.  Thus, the best way to increase the credibility of psychological science is to conduct studies with high power and to minimize selection bias.

This is what I believe Cohen would have said, but even if I am wrong about this, I think it follows from his futile efforts to teach psychologists about type-II errors and statistical power.

How Replicable are Focal Hypothesis Tests in the Journal Psychological Science?

Over the past five years, psychological science has been in a crisis of confidence.  For decades, psychologists have assumed that published significant results provide strong evidence for theoretically derived predictions, especially when authors presented multiple studies with internal replications within a single article (Schimmack, 2012). However, even multiple significant results provide little empirical evidence, when journals only publish significant results (Sterling, 1959; Sterling et al., 1995).  When published results are selected for significance, statistical significance loses its ability to distinguish replicable effects from results that are difficult to replicate or results that are type-I errors (i.e., the theoretical prediction was false).

The crisis of confidence led to several initiatives to conduct independent replications. The most informative replication initiative was conducted by the Open Science Collaborative (Science, 2015).  It replicated close to 100 significant results published in three high-ranked psychology journals.  Only 36% of the replication studies replicated a statistically significant result.  The replication success rate varied by journal.  The journal “Psychological Science” achieved a success rate of 42%.

The low success rate raises concerns about the empirical foundations of psychology as a science.  Without further information, a success rate of 42% implies that it is unclear which published results provide credible evidence for a theory and which findings may not replicate.  It is impossible to conduct actual replication studies for all published studies.  Thus, it is highly desirable to identify replicable findings in the existing literature.

One solution is to estimate replicability for sets of studies based on the published test statistics (e.g., F-statistic, t-values, etc.).  Schimmack and Brunner (2016) developed a statistical method, Powergraphs, that estimates the average replicability of a set of significant results.  This method has been used to estimate replicability of psychology journals using automatic extraction of test statistics (2016 Replicability Rankings, Schimmack, 2017).  The results for Psychological Science produced estimates in the range from 55% to 63% for the years 2010-2016 with an average of 59%.   This is notably higher than the success rate for the actual replication studies, which only produced 42% successful replications.

There are two explanations for this discrepancy.  First, actual replication studies are not exact replication studies and differences between the original and the replication studies may explain some replication failures.  Second, the automatic extraction method may overestimate replicability because it may include non-focal statistical tests. For example, significance tests of manipulation checks can be highly replicable, but do not speak to the replicability of theoretically important predictions.

To address the concern about automatic extraction of test statistics, I estimated replicability of focal hypothesis tests in Psychological Science with hand-coded, focal hypothesis tests.  I used three independent data sets.

Study 1

For Study 1, I hand-coded focal hypothesis tests of all studies in the 2008 Psychological Science articles that were used for the OSC reproducibility project (Science, 2015).


The powergraphs show the well-known effect of publication bias in that most published focal hypothesis tests report a significant result (p < .05, two-tailed, z > 1.96) or at least a marginally significant result (p < .10, two-tailed or p < .05, one-tailed, z > 1.65). Powergraphs estimate the average power of studies with significant results on the basis of the density distribution of significant z-scores.  Average power is an estimate of replicabilty for a set of exact replication studies.  The left graph uses all significant results. The right graph uses only z-scores greater than 2.4 because questionable research practices may produce many just-significant results and lead to biased estimates of replicability. However, both estimation methods produce similar estimates of replicability (57% & 61%).  Given the small number of statistics the 95%CI is relatively wide (left graph: 44% to 73%).  These results are compatible with the low actual success rate for actual replication studies (42%) and the estimate based on automated extraction (59%).

Study 2

The second dataset was provided by Motyl et al. (JPSP, in press), who coded a large number of articles from social psychology journals and psychological science. Importantly, they coded a representative sample of Psychological Science studies from the years 2003, 2004, 2013, and 2014. That is, they did not only code social psychology articles published in Psychological Science.  The dataset included 281 test statistics from Psychological Science.


The powergraph looks similar to the powergraph in Study 1.  More important, the replicability estimates are also similar (57% & 52%).  The 95%CI for Study 1 (44% to 73%) and Study 2 (left graph: 49% to 65%) overlap considerably.  Thus, two independent coding schemes and different sets of studies (2008 vs. 2003-2004/2013/2014) produce very similar results.

Study 3

Study 3 was carried out in collaboration with Sivaani Sivaselvachandran, who hand-coded articles from Psychological Science published in 2016.  The replicability rankings showed a slight positive trend based on automatically extracted test statistics.  The goal of this study was to examine whether hand-coding would also show an increase in replicability.  An increase was expected based on an editorial by D. Stephen Linday, incoming editor in 2015, who aimed to increase replicability of results published in Psychological Science by introducing badges for open data and preregistered hypotheses. However, the results failed to show a notable increase in average replicability.


The replicability estimate was similar to those in the first two studies (59% & 59%).  The 95%CI ranged from 49% to 70%. These wide confidence intervals make it difficult to notice small improvements, but the histogram shows that just significant results (z = 2 to 2.2) are still the most prevalent results reported in Psychological Science and that non-significant results that are to be expected are not reported.

Combined Analysis 

Given the similar results in all three studies, it made sense to pool the data to obtain the most precise estimate of replicability of results published in Psychological Science. With 479 significant test statistics, replicability was estimated at 58% with a 95%CI ranging from 51% to 64%.  This result is in line with the estimated based on automated extraction of test statistics (59%).  The reason for the close match between hand-coded and automated results could be that Psych Science publishes short articles and authors may report mostly focal results because space does not allow for extensive reporting of other statistics.  The hand-coded data confirm that replicabilty in Psychological Science is likely to be above 50%.


It is important to realize that the 58% estimate is an average.  Powergraphs also show average replicability for segments of z-scores. Here we see that replicabilty for just-significant results (z < 2.5 ~ p > .01) is only 35%. Even for z-score between 2.5 and 3.0 (~ p > .001) is only 47%.  Once z-scores are greater than 3, average replicabilty is above 50% and with z-scores greater than 4, replicability is greater than 80%.  For any single study, p-values can vary greatly due to sampling error, but in general a published result with a p-value < .001 is much more likely to replicate than a p-value > .01 (see also OSC, Science, 2015).


This blog-post used hand-coding of test-statistics published in Psychological Science, the flagship journal of the Association for Psychological Science, to estimate replicabilty of published results.  Three dataset produced convergent evidence that the average replicabilty of exact replication studies is 58% +/- 7%.  This result is consistent with estimates based on automatic extraction of test statistics.  It is considerably higher than the success rate of actual replication studies in the OSC reproducibility project (42%). One possible reason for this discrepancy is that actual replication studies are never exact replication studies, which makes it more difficult to obtain statistical significance if the original studies are selected for significance. For example, the original study may have had an outlier in the experimental group that helped to produce a significant result. Not removing this outlier is not considered a questionable research practice, but an exact replication study will not reproduce the same outlier and may fail to reproduce a just-significant result.  More broadly, any deviation from the assumptions underlying the computation of test statistics will increase the bias that is introduced by selecting significant results.  Thus, the 58% estimate is an optimistic estimate of the maximum replicability under ideal conditions.

At the same time, it is important to point out that 58% replicability for Psychological Science does not mean psychological science is rotten to the core (Motyl et al., in press) or that most reported results are false (Ioannidis, 2005).  Even results that did not replicate in actual replication studies are not necessarily false positive results.  It is possible that more powerful studies would produce a significant result, but with a smaller effect size estimate.

Hopefully, these analyses will spur further efforts to increase replicability of published results in Psychological Science and in other journals.  We are already near the middle of 2017 and can look forward to the 2017 results.




How replicable are statistically significant results in social psychology? A replication and extension of Motyl et al. (in press). 

Forthcoming article: 
Motyl, M., Demos, A. P., Carsel, T. S., Hanson, B. E., Melton, Z. J., Mueller, A. B., Prims, J., Sun, J., Washburn, A. N., Wong, K., Yantis, C. A., & Skitka, L. J. (in press). The state of social and personality science: Rotten to the core, not so bad, getting better, or getting worse? Journal of Personality and Social Psychology. (preprint)

Brief Introduction

Since JPSP published incredbile evidence for mental time travel (Bem, 2011), the credibility of social psychological research has been questioned.  There is talk of a crisis of confidence, a replication crisis, or a credibility crisis.  However, hard data on the credibility of empirical findings published in social psychology journals are scarce.

There have been two approaches to examine the credibility of social psychology.  One approach relies on replication studies.  Authors attempt to replicate original studies as closely as possible.  The most ambitious replication project was carried out by the Open Science Collaboration (Science, 2015) that replicated 1 study from 100 articles; 54 articles were classified as social psychology.   For original articles that reported a significant result, only a quarter replicated a significant result in the replication studies.  This estimate of replicability suggests that researches conduct many more studies than are published and that effect sizes in published articles are inflated by sampling error, which makes them difficult to replicate. One concern about the OSC results is that replicating original studies can be difficult.  For example, a bilingual study in California may not produce the same results as a bilingual study in Canada.  It is therefore possible that the poor outcome is partially due to problems of reproducing the exact conditions of original studies.

A second approach is to estimate replicability of published results using statistical methods.  The advantage of this approach is that replicabiliy estimates are predictions for exact replication studies of the original studies because the original studies provide the data for the replicability estimates.   This is the approach used by Motyl et al.

The authors sampled 30% of articles published in 2003-2004 (pre-crisis) and 2013-2014 (post-crisis) from four major social psychology journals (JPSP, PSPB, JESP, and PS).  For each study, coders identified one focal hypothesis and recorded the statistical result.  The bulk of the statistics were t-values from t-tests or regression analyses and F-tests from ANOVAs.  Only 19 statistics were z-tests.   The authors applied various statistical tests to the data that test for the presence of publication bias or whether the studies have evidential value (i.e., reject the null-hypothesis that all published results are false positives).  For the purpose of estimating replicability, the most important statistic is the R-Index.

The R-Index has two components.  First, it uses the median observed power of studies as an estimate of replicability (i.e., the percentage of studies that should produce a significant result if all studies were replicated exactly).  Second, it computes the percentage of studies with a significant result.  In an unbiased set of studies, median observed power and percentage of significant results should match.  Publication bias and questionable research practices will produce more significant results than predicted by median observed power.  The discrepancy is called the inflation rate.  The R-Index subtracts the inflation rate from median observed power because median observed power is an inflated estimate of replicability when bias is present.  The R-Index is not a replicability estimate.  That is, an R-Index of 30% does not mean that 30% of studies will produce a significant result.  However, a set of studies with an R-Index of 30 will have fewer successful replications than a set of studies with an R-Index of 80.  An exception is an R-Index of 50, which is equivalent with a replicability estimate of 50%.  If the R-Index is below 50, one would expect more replication failures than successes.

Motyl et al. computed the R-Index separately for the 2003/2004 and the 2013/2014 results and found “the R-index decreased numerically, but not statistically over time, from .62 [CI95% = .54, .68] in 2003-2004 to .52 [CI95% = .47, .56] in 2013-2014. This metric suggests that the field is not getting better and that it may consistently be rotten to the core.”

I think this interpretation of the R-Index results is too harsh.  I consider an R-Index below 50 an F (fail).  An R-Index in the 50s is a D, and an R-Index in the 60s is a C.  An R-Index greater than 80 is considered an A.  So, clearly there is a replication crisis, but social psychology is not rotten to the core.

The R-Index is a simple tool, but it is not designed to estimate replicability.  Jerry Brunner and I developed a method that can estimate replicability, called z-curve.  All test-statistics are converted into absolute z-scores and a kernel density distribution is fitted to the histogram of z-scores.  Then a mixture model of normal distributions is fitted to the density distribution and the means of the normal distributions are converted into power values. The weights of the components are used to compute the weighted average power. When this method is applied only to significant results, the weighted average power is the replicability estimate;  that is, the percentage of significant results that one would expect if the set of significant studies were replicated exactly.   Motyl et al. did not have access to this statistical tool.  They kindly shared their data and I was able to estimate replicability with z-curve.  For this analysis, I used all t-tests, F-tests, and z-tests (k = 1,163).   The Figure shows two results.  The left figure uses all z-scores greater than 2 for estimation (all values on the right side of the vertical blue line). The right figure uses only z-scores greater than 2.4.  The reason is that just-significant results may be compromised by questionable research methods that may bias estimates.


The key finding is the replicability estimate.  Both estimations produce similar results (48% vs. 49%).  Even with over 1,000 observations there is uncertainty in these estimates and the 95%CI can range from 45 to 54% using all significant results.   Based on this finding, it is predicted that about half of these results would produce a significant result again in a replication study.

However, it is important to note that there is considerable heterogeneity in replicability across studies.  As z-scores increase, the strength of evidence becomes stronger, and results are more likely to replicate.  This is shown with average power estimates for bands of z-scores at the bottom of the figure.   In the left figure,  z-scores between 2 and 2.5 (~ .01 < p < .05) have only a replicability of 31%, and even z-scores between 2.5 and 3 have a replicability below 50%.  It requires z-scores greater than 4 to reach a replicability of 80% or more.   Similar results are obtained for actual replication studies in the OSC reproducibilty project.  Thus, researchers should take the strength of evidence of a particular study into account.  Studies with p-values in the .01 to .05 range are unlikely to replicate without boosting sample sizes.  Studies with p-values less than .001 are likely to replicate even with the same sample size.

Independent Replication Study 

Schimmack and Brunner (2016) applied z-curve to the original studies in the OSC reproducibility project.  For this purpose, I coded all studies in the OSC reproducibility project.  The actual replication project often picked one study from articles with multiple studies.  54 social psychology articles reported 173 studies.   The focal hypothesis test of each study was used to compute absolute z-scores that were analyzed with z-curve.


The two estimation methods (using z > 2.0 or z > 2.4) produced very similar replicability estimates (53% vs. 52%).  The estimates are only slightly higher than those for Motyl et al.’s data (48% & 49%) and the confidence intervals overlap.  Thus, this independent replication study closely replicates the estimates obtained with Motyl et al.’s data.

Automated Extraction Estimates

Hand-coding of focal hypothesis tests is labor intensive and subject to coding biases. Often studies report more than one hypothesis test and it is not trivial to pick one of the tests for further analysis.  An alternative approach is to automatically extract all test statistics from articles.  This makes it also possible to base estimates on a much larger sample of test results.  The downside of automated extraction is that articles also report statistical analysis for trivial or non-critical tests (e.g., manipulation checks).  The extraction of non-significant results is irrelevant because they are not used by z-curve to estimate replicability.  I have reported the results of this method for various social psychology journals covering the years from 2010 to 2016 and posted powergraphs for all journals and years (2016 Replicability Rankings).   Further analyses replicated the results from the OSC reproducibility project that results published in cognitive journals are more replicable than those published in social journals.  The Figure below shows that the average replicability estimate for social psychology is 61%, with an encouraging trend in 2016.  This estimate is about 10% above the estimates based on hand-coded focal hypothesis tests in the two datasets above.  This discrepancy can be due to the inclusion of less original and trivial statistical tests in the automated analysis.  However, a 10% difference is not a dramatic difference.  Neither 50% nor 60% replicability justify claims that social psychology is rotten to the core, nor do they meet the expectation that researchers should plan studies with 80% power to detect a predicted effect.


Moderator Analyses

Motyl et al. (in press) did extensive coding of the studies.  This makes it possible to examine potential moderators (predictors) of higher or lower replicability.  As noted earlier, the strength of evidence is an important predictor.  Studies with higher z-scores (smaller p-values) are, on average, more replicable.  The strength of evidence is a direct function of statistical power.  Thus, studies with larger population effect sizes and smaller sampling error are more likely to replicate.

It is well known that larger samples have less sampling error.  Not surprisingly, there is a correlation between sample size and the absolute z-scores (r = .3).  I also examined the R-Index for different ranges of sample sizes.  The R-Index was the lowest for sample sizes between N = 40 and 80 (R-Index = 43), increased for N = 80 to 200 (R-Index = 52) and further for sample sizes between 200 and 1,000 (R-Index = 69).  Interestingly, the R-Index for small samples with N < 40 was 70.  This is explained by the fact that research designs also influence replicability and that small samples often use more powerful within-subject designs.

A moderator analysis with design as moderator confirms this.  The R-Indices for between-subject designs is the lowest (R-Index = 48) followed by mixed designs (R-Index = 61) and then within-subject designs (R-Index = 75).  This pattern is also found in the OSC reproducibility project and partially accounts for the higher replicability of cognitive studies, which often employ within-subject designs.

Another possibility is that articles with more studies package smaller and less replicable studies.  However,  number of studies in an article was not a notable moderator:  1 study R-Index = 53, 2 studies R-Index = 51, 3 studies R-Index = 60, 4 studies R-Index = 52, 5 studies R-Index = 53.


Motyl et al. (in press) coded a large and representative sample of results published in social psychology journals.  Their article complements results from the OSC reproducibility project that used actual replications, but a much smaller number of studies.  The two approaches produce different results.  Actual replication studies produced only 25% successful replications.  Statistical estimates of replicability are around 50%.   Due to the small number of actual replications in the OSC reproducibility project, it is important to be cautious in interpreting the differences.  However, one plausible explanation for lower success rates in actual replication studies is that it is practically impossible to redo a study exactly.  This may even be true when researchers conduct three similar studies in their own lab and only one of these studies produces a significant result.  Some non-random, but also not reproducible, factor may have helped to produce a significant result in this study.  Statistical models assume that we can redo a study exactly and may therefore overestimate the success rate for actual replication studies.  Thus, the 50% estimate is an optimistic estimate for the unlikely scenario that a study can be replicated exactly.  This means that even though optimists may see the 50% estimate as “the glass half full,” social psychologists need to increase statistical power and pay more attention to the strength of evidence of published results to build a robust and credible science of social behavior.