The Replication Network
Furthering the Practice of Replication in Economics
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Somebody Asks, How Can A Theory Be Falsified? Twitter Responds
[From the Twitter thread started by @JessieSunPsych] Jessie Sun (@JessieSunPsych) relayed the following question that was raised at a recent Psychology conference: “At what point can a theory be falsified (e.g., if the effect size is d = .02)? We often…
Read MoreRedefining RSS
[From the blog “Justify Your Alpha by Decreasing Alpha Levels as a Function of the Sample Size” by Daniël Lakens, posted at The 20% Statistician] “Testing whether observed data should surprise us, under the assumption that some model of the data is…
Read MoreShould Science Do More Replications? It Depends
[From the recent working paper, “The Costs and Benefits of Replication Studies” by Coles, Tiokhin, Scheel, Isager, and Lakens, posted at psyarxiv.com/c8akj] “The debate about whether replication studies should become mainstream is essentially driven by disagreements about their costs and benefits,…
Read MoreA Roundtable Podcast on the Merits of Lowering the Threshold for Statistical Significance to 0.005
This past week, the International Methods Colloquium hosted a conference call on a recent proposal to reduce the threshold of statistical significance to 0.005. Participants included Daniel Benjamin, Daniel Lakens, Blake McShane, Jennifer Tackett, E.J. Wagenmakers, and Justin Esarey, all…
Read More2017 Leamer-Rosenthal Award Winners Announced
Each year, the Berkeley Initiative for Transparency in the Social Sciences (BITSS) awards prizes to researchers who have made substantial contributions to improving transparency in research practices. The prizes are names after Ed Leamer (economics) and Robert Rosenthal (psychology) through…
Read MoreREED: Post-Hoc Power Analyses: Good for Nothing?
Observed power (or post-hoc power) is the statistical power of the test you have performed, based on the effect size estimate from your data. Statistical power is the probability of finding a statistical difference from 0 in your test (aka…
Read MoreLAKENS: Examining the Lack of a Meaningful Effect Using Equivalence Tests
When we perform a study, we would like to conclude there is an effect, when there is an effect. But it is just as important to be able to conclude there is no effect, when there is no effect. So…
Read MoreBayes Factors Versus p-Values
In a recent article in PLOS One, Don van Ravenzwaaij and John Ioannidis argue that Bayes factors should be preferred to significance testing (p-values) when assessing the effectiveness of new drugs. At his blogsite The 20% Statistician, Daniel Lakens argues…
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