52% Yes, a signiicant crisis 3% No, there is no crisis 7% Don't know 38% Yes, a slight crisis 38% Yes, a slight crisis 1,576 RESEARCHERS SURVEYED M ore than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature's survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research. The data reveal sometimes-contradictory attitudes towards reproduc-ibility. Although 52% of those surveyed agree that there is a significant 'crisis' of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature. Data on how much of the scientific literature is reproducible are rare and generally bleak. The best-known analyses, from psychology 1 and cancer biology 2 , found rates of around 40% and 10%, respectively. Our survey respondents were more optimistic: 73% said that they think that at least half of the papers in their field can be trusted, with physicists and chemists generally showing the most confidence. The results capture a confusing snapshot of attitudes around these issues, says Arturo Casadevall, a microbiologist at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland. "At the current time there is no consensus on what reproducibility is or should be. " But just recognizing that is a step forward, he says. "The next step may be identifying what is the problem and to get a consensus. "

Psychology has been stirred by dramatic revelations of questionable research practices (John, Loewenstein, & Prelec, 2012), implausible findings (Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011), and low reproducibility (Open Science Collaboration, 2015; Yong, 2012). The resulting crisis of confidence has led to a wide array of recommendations for improving research practices. Commonly cited advice includes replication, high power, copiloting, adjusting the alpha level, focusing on estimation rather than on testing, and adopting Bayesian statistics (e.g.,

We investigated the reproducibility of the major statistical conclusions drawn in 46 articles published in 2012 in three APA journals. After having identified 232 key statistical claims, we tried to reproduce, for each claim, the test statistic, its degrees of freedom, and the corresponding p value, starting from the raw data that were provided by the authors and closely following the Method section in the article. Out of the 232 claims, we were able to successfully reproduce 163 (70%), 18 of which only by deviating from the article's analytical description. Thirteen (7%) of the 185 claims deemed significant by the authors are no longer so. The reproduction successes were often the result of cumbersome and time-consuming trial-and-error work, suggesting that APA style reporting in conjunction with raw data makes numerical verification at least hard, if not impossible. This article discusses the types of mistakes we could identify and the tediousness of our reproduction efforts in the light of a newly developed taxonomy for reproducibility. We then link our findings with other findings of empirical research on this topic, give practical recommendations on how to achieve reproducibility, and discuss the challenges of large-scale reproducibility checks as well as promising ideas that could considerably increase the reproducibility of psychological research.

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.

According to the crowd within effect, the average of two estimates from one person tends to be more accurate than a single estimate of that person. The effect implies that the well documented wisdom of the crowd effect—the crowd's average estimate tends to be more accurate than the individual estimates—can be obtained within a single individual. In this paper, we performed a high-powered, pre-registered replication study of the original experiment. Our replication results are evaluated with the traditional null hypothesis significance testing approach, as well as with effect sizes and their confidence intervals. We adopted a co-pilot approach, in the sense that all analyses were performed independently by two researchers using different analysis software. Moreover, we report Bayes factors for all tests. We successfully replicated the crowd within effect, both when the second guess was made immediately after the first guess, as well as when it was made 3 weeks later. The experimental protocol, the raw data, the post-processed data and the analysis code are available online.

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect ( p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3–9; median total sample = 1,279.5, range = 276–3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Δ r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols ( r = .05) was similar to that of the RP:P protocols ( r = .04) and the original RP:P replications ( r = .11), and smaller than that of the original studies ( r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00–.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19–.50).

No abstract

Replication efforts in psychological science sometimes fail to replicate prior findings. If replications use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the replication protocol rather than a challenge to the original finding. Formal pre-data collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replications from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) in which the original authors had expressed concerns about the replication designs before data collection and only one of which was “statistically significant” (p &lt; .05). Commenters on RP:P suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these failed to replicate (Gilbert et al., 2016). We revised the replication protocols and received formal peer review prior to conducting new replications. We administered the RP:P and Revised replication protocols in multiple laboratories (Median number of laboratories per original study = XX; Range XX to YY; Median total sample = XX; Range XX to YY) for high-powered tests of each original finding with both protocols. Overall, XX of 10 RP:P protocols and XX of 10 Revised protocols showed significant evidence in the same direction as the original finding (p &lt; .05), compared to an expected XX. The median effect size was [larger/smaller/similar] for Revised protocols (ES = .XX) compared to RP:P protocols (ES = .XX), and [larger/smaller/similar] compared to the original studies (ES = .XX) and [larger/smaller/similar] compared to the original RP:P replications (ES = .XX). Overall, Revised protocols produced [much larger/somewhat larger/similar] effect sizes compared to RP:P protocols (ES = .XX). We also elicited peer beliefs about the replications through prediction markets and surveys of a group of researchers in psychology. The peer researchers predicted that the Revised protocols would [decrease/not affect/increase] the replication rate, [consistent with/not consistent with] the observed replication results. The results suggest that the lack of replicability of these findings observed in RP:P was [partly/completely/not] due to discrepancies in the RP:P protocols that could be resolved with expert peer review.