Category Archives: Psychology


Replicability Ranking of Psychology Departments

Evaluations of individual researchers, departments, and universities are common and arguably necessary as science is becoming bigger. Existing rankings are based to a large extent on peer-evaluations. A university is ranked highly if peers at other universities perceive it to produce a steady stream of high-quality research. At present the most widely used objective measures rely on the quantity of research output and on the number of citations. These quantitative indicators of research quality work are also heavily influenced by peers because peer-review controls what gets published, especially in journals with high rejection rates, and peers decide what research they cite in their own work. The social mechanisms that regulate peer-approval are unavoidable in a collective enterprise like science that does not have a simple objective measure of quality (e.g., customer satisfaction ratings, or accident rates of cars). Unfortunately, it is well known that social judgments are subject to many biases due to conformity pressure, self-serving biases, confirmation bias, motivated biases, etc. Therefore, it is desirable to complement peer-evaluations with objective indicators of research quality.

Some aspects of research quality are easier to measure than others. Replicability rankings focus on one aspect of research quality that can be measured objectively, namely the replicability of a published significant result. In many scientific disciplines such as psychology, a successful study reports a statistically significant result. A statistically significant result is used to minimize the risk of publishing evidence for an effect that does not exist (or even goes in the opposite direction). For example, a psychological study that shows effectiveness of a treatment for depression would have to show that the effect in the study reveals a real effect that can be observed in other studies and in real patients if the treatment is used for the treatment of depression.

In a science that produces thousands of results a year, it is inevitable that some of the published results are fluke findings (even Toyota’s break down sometimes). To minimize the risk of false results entering the literature, psychology like many other sciences, adopted a 5% error rate. By using a 5% as the criterion, psychologists ensured that no more than 5% of results are fluke findings. With thousands of results published in each year, this still means that more than 50 false results enter the literature each year. However, this is acceptable because a single study does not have immediate consequences. Only if these results are replicated in other studies, findings become the foundation of theories and may influence practical decisions in therapy or in other applications of psychological findings (at work, in schools, or in policy). Thus, to outside observers it may appear safe to trust published results in psychology and to report about these findings in newspaper articles, popular books, or textbooks.

Unfortunately, it would be a mistake to interpret a significant result in a psychology journal as evidence that the result is probably true.  The reason is that the published success rate in journals has nothing to do with the actual success rate in psychological laboratories. All insiders know that it is common practice to report only results that support a researcher’s theory. While outsiders may think of scientists as neutral observers (judges), insiders play the game of lobbyist, advertisers, and self-promoters. The game is to advance one’s theory, publish more than others, get more citations than others, and win more grant money than others. Honest reporting of failed studies does not advance this agenda. As a result, the fact that psychological studies report nearly exclusively success stories (Sterling, 1995; Sterling et al., 1995) tells outside observers nothing about the replicability of a published finding and the true rate of fluke findings could be 100%.

This problem has been known for over 50 years (Cohen, 1962; Sterling, 1959). So it would be wrong to call the selective reporting of successful studies an acute crisis. However, what changed is that some psychologists have started to criticize the widely accepted practice of selective reporting of successful studies (Asendorpf et al., 2012; Francis, 2012; Simonsohn et al., 2011; Schimmack, 2012; Wagenmakers et al., 2011). Over the past five years, psychologists, particularly social psychologists, have been engaged in heated arguments over the so-called “replication crisis.”

One group argues that selective publishing of successful studies occurred, but without real consequences on the trustworthiness of published results. The other group argues that published results cannot be trusted unless they have been successfully replicated. The problem is that neither group has objective information about the replicability of published results.  That is, there is no reliable estimate of the percentage of studies that would produce a significant result again, if a representative sample of significant results published in psychology journals were replicated.

Evidently, it is not possible to conduct exact replication studies of all studies that have been published in the past 50 years. Fortunately, it is not necessary to conduct exact replication studies to obtain an objective estimate of replicability. The reason is that replicability of exact replication studies is a function of the statistical power of studies (Sterling et al., 1995). Without selective reporting of results, a 95% success rate is an estimate of the statistical power of the studies that achieved this success rate. Vice versa, a set of studies with average power of 50% is expected to produce a success rate of 50% (Sterling, et al., 1995).

Although selection bias renders success rates uninformative, the actual statistical results provide valuable information that can be used to estimate the unbiased statistical power of published results. Although selection bias inflates effect sizes and power, Brunner and Schimmack (forcecoming) developed and validated a method that can correct for selection bias. This method makes it possible to estimate the replicability of published significant results on the basis of the original reported results. This statistical method was used to estimate the replicabilty of research published by psychology departments in the years from 2010 to 2015 (see Methodology for details).

The averages for the 2010-2012 period (M = 59) and the 2013-2015 period (M = 61) show only a small difference, indicating that psychologists have not changed their research practices in accordance with recommendations to improve replicability in 2011  (Simonsohn et al., 2011). For most of the departments the confidence intervals for the two periods overlap (see attached powergraphs). Thus, the more reliable average across all years is used for the rankings, but the information for the two time periods is presented as well.

There are no obvious predictors of variability across departments. Private universities are at the top (#1, #2, #8), the middle (#24, #26), and at the bottom (#44, #47). European universities can also be found at the top (#4, #5), middle (#25) and bottom (#46, #51). So are Canadian universities (#9, #15, #16, #18, #19, #50).

There is no consensus on an optimal number of replicability.  Cohen recommended that researchers should plan studies with 80% power to detect real effects. If 50% of studies tested real effects with 80% power and the other 50% tested a null-hypothesis (no effect = 2.5% probability to replicate a false result again), the estimated power for significant results would be 78%. The effect on average power is so small because most of the false predictions produce a non-significant result. As a result, only a few studies with low replication probability dilute the average power estimate. Thus, a value greater than 70 can be considered broadly in accordance with Cohen’s recommendations.

It is important to point out that the estimates are very optimistic estimates of the success rate in actual replications of theoretically important effects. For a representative set of 100 studies (OSC, Science, 2015), Brunner and Schimmack’s statistical approach predicted a success rate of 54%, but the success rate in actual replication studies was only 37%. One reason for this discrepancy could be that the statistical approach assumes that the replication studies are exact, but actual replications always differ in some ways from the original studies, and this uncontrollable variability in experimental conditions posses another challenge for replicability of psychological results.  Before further validation research has been completed, the estimates can only be used as a rough estimate of replicability. However, the absolute accuracy of estimates is not relevant for the relative comparison of psychology departments.

And now, without further ado, the first objective rankings of 51 psychology departments based on the replicability of published significant results. More departments will be added to these rankings as the results become available.

Rank University 2010-2015 2010-2012 2013-2015
1 U Penn 72 69 75
2 Cornell U 70 67 72
3 Purdue U 69 69 69
4 Tilburg U 69 71 66
5 Humboldt U Berlin 67 68 66
6 Carnegie Mellon 67 67 67
7 Princeton U 66 65 67
8 York U 66 63 68
9 Brown U 66 71 60
10 U Geneva 66 71 60
11 Northwestern U 65 66 63
12 U Cambridge 65 66 63
13 U Washington 65 70 59
14 Carleton U 65 68 61
15 Queen’s U 63 57 69
16 U Texas – Austin 63 63 63
17 U Toronto 63 65 61
18 McGill U 63 72 54
19 U Virginia 63 61 64
20 U Queensland 63 66 59
21 Vanderbilt U 63 61 64
22 Michigan State U 62 57 67
23 Harvard U 62 64 60
24 U Amsterdam 62 63 60
25 Stanford U 62 65 58
26 UC Davis 62 57 66
27 UCLA 61 61 61
28 U Michigan 61 63 59
29 Ghent U 61 58 63
30 U Waterloo 61 65 56
31 U Kentucky 59 58 60
32 Penn State U 59 63 55
33 Radboud U 59 60 57
34 U Western Ontario 58 66 50
35 U North Carolina Chapel Hill 58 58 58
36 Boston University 58 66 50
37 U Mass Amherst 58 52 64
38 U British Columbia 57 57 57
39 The University of Hong Kong 57 57 57
40 Arizona State U 57 57 57
41 U Missouri 57 55 59
42 Florida State U 56 63 49
43 New York U 55 55 54
44 Dartmouth College 55 68 41
45 U Heidelberg 54 48 60
46 Yale U 54 54 54
47 Ohio State U 53 58 47
48 Wake Forest U 51 53 49
49 Dalhousie U 50 45 55
50 U Oslo 49 54 44
51 U Kansas 45 45 44


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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.


New (February, 2, 2016)
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?




1. 2015 Replicability Rankings of over 100 Psychology Journals
Based on reported test statistics in all articles from 2015, the rankings show the typical strength of evidence for a statistically significant result in particular journals.  The method also estimates the file-drawer of unpublished non-significant results. Links to powergraphs provide further information (e.g., whether a journal has too many just significant results (p .025).


2. A (preliminary) Introduction to the Estimation of Replicability for Sets of Studies with Heterogeneity in 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.  Replicability-Rankings of Psychology Departments
This blog presents rankings of psychology departments on the basis of the replicability of significant results published in 105 psychology journals (see the journal rankings for a list of journals).   Reported success rates in psychology journals are over 90%, but this percentage is inflated by selective reporting of significant results.  After correcting for selection bias, replicability is 60%, but there is reliable variation across departments.


4. 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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5.  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.

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6.  Validation of Meta-Analysis of Observed (post-hoc) Power
This post examines the ability of various estimation methods to estimate power of a set of studies based on the reported test statistics in these studies.  The results show that most estimation methods work well when all studies have the same effect size (homogeneous) or if effect sizes are heterogeneous and symmetrically distributed (heterogeneous). However, most methods fail when effect sizes are heterogeneous and have a skewed distribution.  The post does not yet include the more recent method that uses the distribution of z-scores (powergraphs) to estimate observe power because this method was developed after this blog was posted.


7. Roy Baumeister’s R-Index
Roy Baumeister was a reviewer of my 2012 article that introduced the Incredibiliy Index to detect publication bias and dishonest reporting practices.  In his review and in a subsequent email exchange, Roy Baumeister admitted that his published article excluded studies that failed to produce results in support of his theory that blood-glucose is important for self-regulation (a theory that is now generally considered to be false), although he disagrees that excluding these studies was dishonest.  The R-Index builds on the incredibility index and provides an index of the strength of evidence that corrects for the influence of dishonest reporting practices.  This post reports the R-Index for Roy Baumeister’s most cited articles. The R-Index is low and does not justify the nearly perfect support for empirical predictions in these articles. At the same time, the R-Index is similar to R-Indices for other sets of studies in social psychology.  This suggests that dishonest reporting practices are the norm in social psychology and that published articles exaggerate the strength of evidence in support of social psychological theories. 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.


9.  The R-Index for 18 Multiple-Study Psychology Articles in the Journal SCIENCE.
Francis (2014) demonstrated that nearly all multiple-study articles by psychology researchers that were published in the prestigious journal SCIENCE showed evidence of dishonest reporting practices (disconfirmatory evidence was missing).  Francis (2014) used a method similar to the incredibility index.  One problem of this method is that the result is a probability that is influenced by the amount of bias and the number of results that were available for analysis. As a result, an article with 9 studies and moderate bias is treated the same as an article with 4 studies and a lot of bias.  The R-Index avoids this problem by focusing on the amount of bias (inflation) and the strength of evidence.  This blog post shows the R-Index of the 18 studies and reveals that many articles have a low R-Index.


10.  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.


“Do Studies of Statistical Power Have an Effect on the Power of Studies?” by Peter Sedlmeier and Gerg Giegerenzer

The article with the witty title “Do Studies of Statistical Power Have an Effect on the Power of Studies?” builds on Cohen’s (1962) seminal power analysis of psychological research.

The main point of the article can be summarized in one word: No. Statistical power has not increased after Cohen published his finding that statistical power is low.

One important contribution of the article was a meta-analysis of power analyses that applied Cohen’s method to a variety of different journals. The table below shows that power estimates vary by journal assuming that the effect size was medium according to Cohen’s criteria of small, medium, and large effect sizes. The studies are sorted by power estimates from the highest to the lowest value, which provides a power ranking of journals based on Cohen’s method. I also included the results of Sedlmeier and Giegerenzer’s power analysis of the 1984 volume of the Journal of Abnormal Psychology (the Journal of Social and Abnormal Psychology was split into Journal of Abnormal Psychology and Journal of Personality and Social Psychology). I used the mean power (50%) rather than median power (44%) because the mean power is consistent with the predicted success rate in the limit. In contrast, the median will underestimate the success rate in a set of studies with heterogeneous effect sizes.

Journal of Marketing Research 1981 89
American Sociological Review 1974 84
Journalism Quarterly, The Journal of Broadcasting 1976 76
American Journal of Educational Psychology 1972 72
Journal of Research in Teaching 1972 71
Journal of Applied Psychology 1976 67
Journal of Communication 1973 56
The Research Quarterly 1972 52
Journal of Abnormal Psychology 1984 50
Journal of Abnormal and Social Psychology 1962 48
American Speech and Hearing Research & Journal of Communication Disorders 1975 44
Counseler Education and Supervision 1973 37


The table shows that there is tremendous variability in power estimates for different journals ranging from as high as 89% (9 out of 10 studies will produce a significant result when an effect is present) to the lowest estimate of  37% power (only 1 out of 3 studies will produce a significant result when an effect is present).

The table also shows that the Journal of Abnormal and Social Psychology and its successor the Journal of Abnormal Psychology yielded nearly identical power estimates. This finding is the key finding that provides empirical support for the claim that power in the Journal of Abnormal Psychology has not increased over time.

The average power estimate for all journals in the table is 62% (median 61%).  The list of journals is not a representative set of journals and few journals are core psychology journals. Thus, the average power may be different if a representative set of journals had been used.

The average for the three core psychology journals (JASP & JAbnPsy,  JAP, AJEduPsy) is 67% (median = 63%) is slightly higher. The latter estimate is likely to be closer to the typical power in psychology in general rather than the prominently featured estimates based on the Journal of Abnormal Psychology. Power could be lower in this journal because it is more difficult to recruit patients with a specific disorder than participants from undergraduate classes. However, only more rigorous studies of power for a broader range of journals and more years can provide more conclusive answers about the typical power of a single statistical test in a psychology journal.

The article also contains some important theoretical discussions about the importance of power in psychological research. One important issue concerns the treatment of multiple comparisons. For example, a multi-factorial design produces an exponential number of statistical comparisons. With two conditions, there is only one comparison. With three conditions, there are three comparisons (C1 vs. C2, C1 vs. C3, and C2 vs. C3). With 5 conditions, there are 10 comparisons. Standard statistical methods often correct for these multiple comparisons. One consequence of this correction for multiple comparisons is that the power of each statistical test decreases. An effect that would be significant in a simple comparison of two conditions would not be significant if this test is part of a series of tests.

Sedlmeier and Giegerenzer used the standard criterion of p < .05 (two-tailed) for their main power analysis and for the comparison with Cohen’s results. However, many articles presented results using a more stringent criterion of significance. If the criterion used by authors would have been used for the power analysis, power decreased further. About 50% of all articles used an adjusted criterion value and if the adjusted criterion value was used power was only 37%.

Sedlmeier and Giegerenzer also found another remarkable difference between articles in 1960 and in 1984. Most articles in 1960 reported the results of a single study. In 1984 many articles reported results from two or more studies. Sedlmeier and Giegerenzer do not discuss the statistical implications of this change in publication practices. Schimmack (2012) introduced the concept of total power to highlight the problem of publishing articles that contain multiple studies with modest power. If studies are used to provide empirical support for an effect, studies have to show a significant effect. For example, Study 1 shows an effect with female participants. Study 2 examines whether the effect can also be demonstrated with male participants. If Study 2 produces a non-significant result, it is not clear how this finding should be interpreted. It may show that the effect does not exist for men. It may show that the first result was just a fluke finding due to sampling error. Or it may show that the effect exists equally for men and women but studies had only 50% power to produce a significant result. In this case, it is expected that one study will produce a significant result and one will produce a non-significant result, but in the long-run significant results are equally likely with male or female participants. Given the difficulty of interpreting a non-significant result, it would be important to conduct a more powerful study that examines gender differences in a more powerful study with more female and male participants. However, this is not what researchers do. Rather, multiple study articles contain only the studies that produced significant results. The rate of successful studies in psychology journals is over 90% (Sterling et al., 1995). However, this outcome is extremely likely in multiple studies where studies have only 50% power to get a significant result in a single attempt. For each additional attempt, the probability to obtain only significant results decreases exponentially (1 Study, 50%, 2 Studies 25%, 3 Studies 12.5%, 4 Studies 6.75%).

The fact that researchers only publish studies that worked is well-known in the research community. Many researchers believe that this is an acceptable scientific practice. However, consumers of scientific research may have a different opinion about this practice. Publishing only studies that produced the desired outcome is akin to a fund manager that only publishes the return rate of funds that gained money and excludes funds with losses. Would you trust this manager to take care of your retirement? It is also akin to a gambler that only remembers winnings. Would you marry a gambler who believes that gambling is ok because you can earn money that way?

I personally do not trust obviously biased information. So, when researchers present 5 studies with significant results, I wonder whether they really had the statistical power to produce these results or whether they simply did not publish results that failed to confirm their claims. To answer this question it is essential to estimate the actual power of individual studies to produce significant results; that is, it is necessary to estimate the typical power in this field, of this researcher, or in the journal that published the results.

In conclusion, Sedlmeier and Gigerenzer made an important contribution to the literature by providing the first power-ranking of scientific journals and the first temporal analyses of time trends in power. Although they probably hoped that their scientific study of power would lead to an increase in statistical power, the general consensus is that their article failed to change scientific practices in psychology. In fact, some journals required more and more studies as evidence for an effect (some articles contain 9 studies) without any indication that researchers increased power to ensure that their studies could actually provide significant results for their hypotheses. Moreover, the topic of statistical power remained neglected in the training of future psychologists.

I recommend Sedlmeier and Gigerenzer’s article as essential reading for anybody interested in improving the credibility of psychology as a rigorous empirical science.

As always, comments (positive or negative) are always welcome.

The Association for Psychological Science Improves Success Rate from 95% to 100% by Dropping Hypothesis Testing: The Sample Mean is the Sample Mean, Type-I Error 0%

The editor of Psychological Science published an Editorial with the title “Business Not as Usual.” (see also Observer interview and new Submission Guidelines) The new submission guidelines recommend the following statistical approach.

Effective January 2014, Psychological Science recommends the use of the “new statistics”—effect sizes, confidence intervals, and meta-analysis—to avoid problems associated with null-hypothesis significance testing (NHST). Authors are encouraged to consult this Psychological Science tutorial by Geoff Cumming, which shows why estimation and meta-analysis are more informative than NHST and how they foster development of a cumulative, quantitative discipline. Cumming has also prepared a video workshop on the new statistics that can be found here.

The editorial is a response to the current crisis in psychology that many findings cannot be replicated and the discovery that numerous articles in Psychological Science show clear evidence of reporting biases that lead to inflated false-positive rates and effect sizes (Francis, 2013).

The editorial is titled “Business not as usual.”  So what is the radical response that will ensure increased replicability of results published in Psychological Science? One solution is to increase transparency and openness to discourage the use of deceptive research practices (e.g., not publishing undesirable results or selective reporting of dependent variables that showed desirable results). The other solution is to abandon null-hypothesis significance testing.

Problem of the Old Statistics: Researchers had to demonstrate that their empirical results could have occurred only with a 5% probability if there is no effect in the population.

Null-hypothesis testing has been the main method to relate theories to empirical data. An article typically first states a theory and then derives a theoretical prediction from the theory. The theoretical prediction is then used to design a study that can be used to test the theoretical prediction. The prediction is tested by computing the ratio of the effect size and sampling error (signal-to-noise) ratio. The next step is to determine the probability of obtaining the observed signal-to-noise ratio or an even more extreme one under the assumption that the true effect size is zero. If this probability is smaller than a criterion value, typically p < .05, the results are interpreted as evidence that the theoretical prediction is true. If the probability does not meet the criterion, the data are considered inconclusive.

However, non-significant results are irrelevant because Psychological Science is only interested in publishing research that supports innovative novel findings. Nobody wants to know that drinking fennel tea does not cure cancer, but everybody wants to know about a treatment that actually cures cancer. So, the main objective of statistical analyses was to provide empirical evidence for a predicted effect by demonstrating that an obtained result would occur only with a 5% probability if the hypothesis were false.

Solution to the problem of Significance Testing: Drop the Significance Criterion. Just report your sample mean and the 95% confidence interval around it.


Eich claims that “researchers have recognized,…, essential problems with NHST in general, and with dichotomous thinking (“significant” vs. “non-significant” ) thinking it engenders in particular. It is true that statisticians have been arguing about the best way to test theoretical predictions with empirical data. In fact, they are still arguing. Thus, it is interesting to examine how Psychological Science found a solution to the elusive problem of statistical inference. The answer is to avoid statistical inferences altogether and to avoid dichotomous thinking. Does fennel tea cure cancer? Maybe, 95%CI d = -.4 to d = +4. No need to test for statistical significance. No need to worry about inadequate sample sizes. Just do a study and report your sample means with a confidence interval. It is that easy to fix the problems of psychological science.

The problem is that every study produces a sample mean and a confidence interval. So, how do the editors of Psychological Science pick the 5% of submitted manuscripts that will be accepted for publication? Eich lists three criteria.

  1. What will the reader of this article learn about psychology that he or she did not know (or could not have known) before?

The effect of manipulation X on dependent variable Y is d = .2, 95%CI = -.2 to .6. We can conclude from this result that it is unlikely that the manipulation leads to a moderate decrease or a strong increase in the dependent variable Y.

  1. Why is that knowledge important for the field?

The finding that the experimental manipulation of Y in the laboratory is somewhat more likely to produce an increase than a decrease, but could also have no effect at all has important implications for public policy.

  1. How are the claims made in the article justified by the methods used?

The claims made in this article are supported by the use of Cumming’s New Statistics. Based on a precision analysis, the sample size was N = 100 (n = 50 per condition) to achieve a precision of .4 standard deviations. The study was preregistered and the data are publicly available with the code to analyze the data (SPPS t-test groups x (1,2) / var y.).

If this sounds wrong to you and you are a member of APS, you may want to write to Erich Eich and ask for some better guidelines that can be used to evaluate whether a sample mean or two or three or four sample means should be published in your top journal.