Bayesian inference offers the pragmatic researcher a series of perks (Wagenmakers, Morey, & Lee, 2016). For instance, Bayesian hypothesis tests can quantify support in favor of a null hypothesis, and they allow researchers to track evidence as data accumulate (e.g., Rouder, 2014). However, Bayesian inference also confronts researchers with new challenges, for instance concerning the planning of experiments. Within the…
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In our previous post, we discussed the paper “Abandon Statistical Significance”, which is a response to the paper “Redefine Statistical Significance” that has dominated the contents of this blog so far. The Abandoners include Andrew Gelman and Christian Robert, and on their own blogs they’ve each posted a reaction to our Bayesian Spectacles post. Below is a short response to…
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Andrew Gelman and Christian Robert are two of the most opinionated and influential statisticians in the world today. Fear and anguish strike into the heart of the luckless researchers who find the fruits of their labor discussed on the pages of the duo’s blogs: how many fatal mistakes will be uncovered, how many flawed arguments will be exposed? Personally, we…
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The key point of the paper “Redefine Statistical Significance” is that p-just-below-.05 results should be approached with care. They should perhaps evoke curiosity, but they should not receive the blanket endorsement that is implicit in the bold claim “we reject the null hypothesis”. The statistical argument is straightforward and has been known for over half a century: for p-just-below-.05 results,…
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The paper Redefine Statistical Significance reveals an inconvenient truth: p-values near .05 are evidentially weak. Such p-values should not be used “for sanctification, for the preservation of conclusions from all criticism, for the granting of an imprimatur.” (Tukey, 1962, p. 13 — NB: Tukey was referring to statistical procedures in general, not to p-values or p-just-below-.05 results specifically). Unfortunately, in…
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In our previous posts about the paper “Redefine Statistical Significance”, two concrete examples corroborated the general claim that p-just-below-.05 results constitute weak evidence against the null hypothesis. We compared the predictive performance of H0 (effect size = 0) to the predictive performance of H1 (specified by a range of different prior distributions on effect size) and found that for a…
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In previous posts we provided detailed Bayesian reanalyses of two “p-just-below-.05” experiments (i.e., red, rank, and romance, and flag-priming). For both experiments, the evidence against the null hypothesis was relatively weak, and this supported the main claim from the paper “Redefine Statistical Significance” (and the 2016 claim by the American Statistical Association, and the claim made by statisticians throughout the last…
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In the two previous posts on the paper “Redefine Statistical Significance”, we reanalyzed Experiment 1 from “Red, Rank, and Romance in Women Viewing Men” (Elliot et al., 2010). Female undergrads rated the attractiveness of a single male from a black-and-white photo. Ten women saw the photo on a red background, and eleven saw the photo on a white background. The…
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