Bayesian Data Analysis: A Fresh Approach to Power Issues and Null Hypothesis Interpretation

One of the first things one learns in a basic psychology or statistics course is that you cannot prove the null hypothesis that there is no difference between two conditions such as a patient group and a normal control group. This remains true. However now, thanks to ongoing progress by a special gr...

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Bibliographic Details
Published in:Applied psychophysiology and biofeedback 2021-06, Vol.46 (2), p.135-140
Main Authors: Peter Rosenfeld, J., Olson, Joseph M.
Format: Article
Language:English
Subjects:
Bayesian analysis
Behavioral Science and Psychology
Health Psychology
Hypotheses
Psychology
Psychotherapy and Counseling
Public Health
Statistical analysis
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Summary:One of the first things one learns in a basic psychology or statistics course is that you cannot prove the null hypothesis that there is no difference between two conditions such as a patient group and a normal control group. This remains true. However now, thanks to ongoing progress by a special group of devoted methodologists, even when the result of an inferential test is p > .05, it is now possible to rigorously and quantitatively conclude that (a) the null hypothesis is actually unlikely, and (b) that the alternative hypothesis of an actual difference between treatment and control is more probable than the null. Alternatively, it is also possible to conclude quantitatively that the null hypothesis is much more likely than the alternative. Without Bayesian statistics, we couldn’t say anything if a simple inferential analysis like a t-test yielded p > .05. The present, mostly non-quantitative article describes free resources and illustrative procedures for doing Bayesian analysis, with t-test and ANOVA examples.
ISSN:1090-0586
1573-3270
DOI:10.1007/s10484-020-09502-y