The Replication Network
Furthering the Practice of Replication in Economics
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How Many Ways Can You Misinterpret p-Values, Confidence Intervals, Statistical Tests, and Power? 25
[From the blog “Misinterpreting Tests, P-Values, Confidence Intervals & Power” by Dave Giles, posted at his blogsite, Econometrics Beat] “Today I was reading a great paper by Greenland et al. (2016) that deals with some common misconceptions and misinterpretations that arise not…
Read MoreUsing Bayesian Reanalysis to Decide Which Studies to Replicate
[From the preprint “When and Why to Replicate: As Easy as 1, 2, 3?” by Sarahanne Field, Rink Hoekstra, Laura Bringmann, and Don van Ravenzwaaij, posted at PsyArXiv Preprints.] “…a flood of new replications of existing research have reached the…
Read MoreHIRSCHAUER et al.: Why replication is a nonsense exercise if we stick to dichotomous significance thinking and neglect the p-value’s sample-to-sample variability
[This blog is based on the paper “Pitfalls of significance testing and p-value variability: An econometrics perspective” by Norbert Hirschauer, Sven Grüner, Oliver Mußhoff, and Claudia Becker, Statistics Surveys 12(2018): 136-172.] Replication studies are often regarded as the means to…
Read MoreFailure of Justice: p-Values and the Courts
[From the abstract of the working paper, “US Courts of Appeal cases frequently misinterpret p-values and statistical significance: An empirical study”, by Adrian Barnett and Steve Goodman, posted at Open Science Framework] “We examine how p-values and statistical significance have been interpreted…
Read More80% Power? Really?
[From the blog “The “80% power” lie” posted by Andrew Gelman in December 2017 at Statistical Modeling, Causal Inference, and Social Science] “Suppose we really were running studies with 80% power. In that case, the expected z-score is 2.8, and…
Read MoreP-Values Between 0.01 and 0.10 Are a Problem?
[From the blog, “The uncanny mountain: p-values between .01 and .10 are still a problem” by Julia Rohrer, posted at The 100% CI] “Study 1: In line with our hypothesis, …, p = 0.03.” “Study 2: As expected, … p =…
Read MoreA Summary of Proposals to Improve Statistical Inference
In a recent comment published in the Journal of the American Medical Association, John Ioannidis provided the following summary of proposals (see table below). The summary, and his brief commentary, may be of interest to readers of TRN. Source: Ioannidis…
Read More2 Humps = P-Hacking + Publication Bias?
In a recent blogpost at Simply Statistics, Jeff Leek announced a new R package called tidypvals: “The tidypvals package is an effort to find previous collections of published p-values, synthesize them, and tidy them into one analyzable data set.” In a preview…
Read MoreIf the American Statistical Association Warns About p-Values, and Nobody Hears It, Does It Make a Sound?
[From the article, “The ASA’s p-value statement, one year on”, which appeared in the online journal Significance, a publication of the American Statistical Association] “A little over a year ago now, in March 2016, the American Statistical Association (ASA) took…
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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