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Karl Pearson’s Best Quotation?

NB. The next post will discuss two of Karl Pearson’s worst quotations.

 
 
 
 
 
 
 
 

“The field of science is unlimited; its material is endless, every group of natural phenomena, every phase of social life, every stage of past or present development is material for science. The unity of all science consists alone in its method, not in its material. The man who classifies facts of any kind whatever, who sees their mutual relation and describes their sequences, is applying the scientific method and is a man of science. The facts may belong to the past history of mankind, to the social statistics of our great cities, to the atmosphere of the most distant stars, to the digestive organs of a worm, or to the life of a scarcely visible bacillus. It is not the facts themselves which form science, but the method in which they are dealt with.” (Karl Pearson, The Grammar of Science, p. 16)

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Quantifying Support for the Null Hypothesis in Psychology: An Empirical Investigation

This post summarizes the content of an article that is in press for Advances in Methods and Practices in Psychological Science.1 The preprint is available on PsyArXiv.

In the traditional statistical framework, nonsignificant results leave researchers in a state of suspended disbelief. This study examines, empirically, the treatment and evidential impact of nonsignificant results. Our specific goals were twofold: to explore how psychologists interpret and communicate nonsignificant results, and to assess how much these results constitute evidence in favor of the null hypothesis. Firstly, we examined all nonsignificant findings mentioned in the abstracts of the 2015 volume of Psychonomic Bulletin & Review, Journal of Experimental Psychology: General, and Psychological Science (N = 137). In 72% of cases, nonsignificant results were misinterpreted, in the sense that authors inferred that the effect was absent. Secondly, a Bayes factor reanalysis revealed that fewer than 5% of the nonsignificant findings provided strong evidence (i.e., BF01 > 10) in favor of the null hypothesis compared to the alternative hypothesis. We recommend that researchers expand their statistical toolkit in order to correctly interpret nonsignificant results and to be able to evaluate the evidence for and against the null hypothesis.

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Bayesian Reanalysis of Null Results Reported in Medicine: Strong Yet Variable Evidence for the Absence of Treatment Effects

This post summarizes the content of an article that is in press for PLOS ONE. The preprint is available on PsyArXiv.

Efficient medical progress requires that we know when a treatment effect is absent. We considered all 207 Original Articles published in the 2015 volume of the New England Journal of Medicine and found that 45 (21.7%) reported a null result for at least one of the primary outcome measures. Unfortunately, standard statistical analyses are unable to quantify the degree to which these null results actually support the null hypothesis. Such quantification is possible, however, by conducting a Bayesian hypothesis test. Here we reanalyzed a subset of 43 null results from 36 articles using a default Bayesian test for contingency tables. This Bayesian reanalysis revealed that, on average, the reported null results provided strong evidence for the absence of an effect. However, the degree of this evidence is variable and cannot be reliably predicted from the p-value (see Figure 1). For null results, sample size is a better (albeit imperfect) predictor for the strength of evidence in favor of the null hypothesis (see Figure 2). Together, our findings suggest that (a) the reported null results generally correspond to strong evidence in favor of the null hypothesis; (b) a Bayesian hypothesis test can provide additional information to assist the interpretation of null results.

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Bayesian Tutorials Galore

This post highlights a recent special issue on Bayesian inference edited by Joachim Vandekerckhove, Jeff Rouder, and John Kruschke for Psychonomic Bulletin & Review. What sets this special issue apart is that most of the 16 contributions (spanning a total of 285 pages!) have a tutorial character. Researchers and students who are new to Bayesian inference –its theoretical underpinnings, its advantages in practice, its application to concrete data analysis problems– could do worse than study this special issue cover to cover. Note: several contributions are open access; those behind a paywall may be easily obtained through Sci-Hub.

In their introduction, the editors describe the contents of the special issue as follows:

 
 
 

“The special issue is divided into four sections. The first section is a coordinated five-part introduction that starts from the most basic concepts and works up to the general structure of complex problems and to contemporary issues. The second section is a selection of advanced topics covered in-depth by some of the world’s leading experts on statistical inference in psychology. The third section is an extensive collection of teaching resources, reading lists, and strong arguments for the use of Bayesian methods at the expense of classical methods. The final section contains a number of applications of advanced Bayesian analyses that provide an idea of the wide reach of Bayesian methods for psychological science.”

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