Redefine Statistical Significance Part X: Why the Point-Null Will Never Die

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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Redefine Statistical Significance Part IX: Gelman and Robert Join the Fray, But Are Quickly Chased by Two Kangaroos

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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Redefine Statistical Significance Part VIII: How 88 Authors Overlooked a Giraffe and Sailed Straight into an Iceberg

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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Redefine Statistical Significance Part VII: Bursting the Bubble

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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Redefine Statistical Significance Part VI: Another Nail in the .05 Coffin

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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Redefine Statistical Significance Part V: A Wizard Walks Into a Sauna and Starts Pawing at a Pizza…

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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Redefine Statistical Significance Part IV: A Second Demonstration

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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Redefine Statistical Significance Part III: Informed Priors and Oracle Priors

Has the common criterion for statistical significance –“1-in-20”– tempted researchers into making strong claims from weak evidence? Should p-values near .05 be considered only suggestive? Are researchers caught in a bad romance? Last year, the American Statistical Association stated that “a p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis” (Wasserstein and Lazar, 2016, p.…

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