Predicting Reproducibility. No PhD Required.

[Excerpts taken from the article “Laypeople Can Predict Which Social Science Studies Replicate” by Suzanne Hoogeveen, Alexandra Sarafoglou, and Eric-Jan Wagenmakers, posted at PsyArXiv Preprints]
“…we assess the extent to which a finding’s replication success relates to its intuitive plausibility. Each of 27 high-profile social science findings was evaluated by 233 people without a PhD in psychology. Results showed that these laypeople predicted replication success with above-chance performance (i.e., 58%). In addition, when laypeople were informed about the strength of evidence from the original studies, this boosted their prediction performance to 67%.”
“Participants were presented with 27 studies, a subset of the studies included in the Social Sciences Replication Project…and the Many Labs 2 Project.”
“For each study, participants read a short description of the research question, its operationalization, and the key finding…In the Description Only condition, solely the descriptive texts were provided; in the Description Plus Evidence condition, the Bayes factor and its verbal interpretation (e.g., “moderate evidence”) for the original study were added to the descriptions.”
“After the instructions, participants…indicated whether they believed that this study would replicate or not (yes / no), and expressed their confidence in their decision on a slider ranging from 0 to 100.”
“Figure 1 displays participants’ confidence ratings concerning the replicability of each of the 27 included studies, ordered according to the averaged confidence score.”
“Positive ratings reflect confidence in replicability, and negative ratings reflect confidence in non-replicability, with −100 denoting extreme confidence that the effect would fail to replicate. Note that these data are aggregated across the Description Only and the Description Plus Evidence condition.”
“The top ten rows indicate studies for which laypeople showed relatively high agreement that the associated studies would replicate. Out of these ten studies, nine replicated and only one did not (i.e., the study by Anderson, Kraus, Galinsky, & Keltner, 2012; note that light-grey indicates a successful replication, and dark-grey indicates a failed replication).”
“The bottom four rows indicate studies for which laypeople showed relatively high agreement that the associated studies would fail to replicate. Consistent with laypeople’s predictions, none of these four studies replicated.”
“For the remaining 13 studies in the middle rows, the group response was relatively ambiguous, as reflected by a bimodal density that is roughly equally distributed between the negative and positive end of the scale. Out of these 13 studies, five replicated successfully and eight failed to replicate successfully.”
“Overall, Figure 1 provides a compelling demonstration that laypeople are able to predict whether or not high-profile social science findings will replicate successfully.”
“…the relative ordering of laypeople’s confidence in replicability for a given set of studies may provide estimations of the relative probabilities of replication success.”
“If a replicator’s goal is to purge the literature of unreliable effects, he or she may start by conducting replications of the studies for which replication failure is predicted by naive forecasters.”
“Alternatively, if the goal is to clarify the reliability of studies for which replication outcomes are most uncertain, one could select studies for which the distribution of the expected replicability is characterized by a bi-modal shape.”
“As such, prediction surveys may serve as ‘decision surveys’, instrumental in the selection stage of replication research (cf. Dreber et al., 2015). These informed decisions could not only benefit the replicator, but also optimize the distribution of funds and resources for replication projects.”
To read the article, click here.



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