Is the Replicability Crisis Overblown? Three Arguments Examined

By Harold Pashler, Christine R. Harris. Perspectives on Psychological Science, 2012

658 citations recorded by the source corpus.

Subfields: Economics and Econometrics, History and Philosophy of Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8e8aefd6621e1eed5bd2e630b58a7f966cb79bd6

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Citations in the corpus

Facts Are More Important Than Novelty (2014, background): “Because of this, some argue direct replication should always precede conceptual replication attempts (e.g., Pashler & Harris, 2012).”
Editors’ Introduction to the Special Section on Replicability in Psychological Science (2012, background): “…mathematical modeling that any scientific field that ignores replication can easily come to the miserable state wherein (as the title of his most famous article puts it) “most published research findings are false” (see also Ioannidis, 2012, this issue, and Pashler & Harris, 2012, this issue).”
A Multilab Preregistered Replication of the Ego-Depletion Effect (2016, background): “The literature on the ego-depletion effect is a reflection of broader current debates over the reproducibility of effects in psychological experiments (Pashler & Harris, 2012) and the need for high-powered replications of prominent effects in the discipline (Open Science Collaboration, 2012, 2015).”
Constraints on Generality (COG): A Proposed Addition to All Empirical Papers (2017, result): “…see Lilienfeld, Marshall, Todd, & Shane, 2014), and replication attempts that vary some aspect of the procedure and fail to observe the same result are often taken not as constraints on the generality of the original findings but as evidence that the effect does not exist (Pashler & Harris, 2012).”
Bilingualism and cognition (2014, methodology): “See also Pashler and Harris, 2012; Ledgerwood, 2014; and the series of articles on methods published with Ledgerwood, 2014, in Perspectives in Psychological Science, 9 (3), 2014.”
Working with Misspecified Regression Models (2018, background): “8Model selection in some disciplines is called variable selection, feature selection, or dimension reduction. reductions, one would be hard pressed to claim reproducibility (Ioannidis, 2014; Harris, 2012; Open Science Collaboration, 2015).”
How Bayes factors change scientific practice (2016, background): “…the problems of what we have been doing up to now are evident (e.g. Ioannidis, 2005; John et al., 2012; Open Science Collaboration, 2015; Pashler & Harris, 2012), I hope Bayes is seriously considered as part of the solution— alongwith, for example, full transparency and online availability of…”
Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of “Far Transfer”: Evidence from a Meta-Analytic Review (2016, neutral): “In the past few years, there has been much attention paid to issues of replicability in psychology (e.g., Ioannidis, 2012; Open Science Collaboration, 2015; Pashler & Harris, 2012).”
Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations (2017, background): “Based on these ﬁndings and the relative ease of collecting data quickly on MTurk, we speculate that the increasing pressure for replication (Pashler and Harris, 2012; Klein et al., 2014; Stanley and Spence, 2014; Maxwell et al., 2015) may be a partial cause of MTurk’s increasing popularity.”
Reflections on the replication corner: In praise of conceptual replications (2015, methodology): “Pashler and Harris (2012) have correctly noted that absent any systematic replication effort, the normal journal review process makes it more likely that successful conceptual replications will be published than unsuccessful ones because successful conceptual replications seem more interesting.”
Play It Again, Sam! An Analysis of Constructive Replication in the Organizational Sciences (2019, background): “Carpenter (2012), Pashler and Harris (2012), and others describe some of the objections to claims of a crisis, claims that the authors of the present paper have heard voiced in various panel discussions on the topic.”
Using Bayes to get the most out of non-significant results (2014, background): “…have been ignored when they were in fact informative (e.g., believing that an apparent failure to replicate with a non-significant result is more likely to indicate noise produced by sloppy experimenters than a true null hypothesis; cf. Greenwald, 1993; Pashler and Harris, 2012; Kruschke, 2013a).”