A Bayesian Analysis of Evidence in Support of the Null Hypothesis in Gerontological Psychology (or Lack Thereof). (16th March 2019)

• Record Type: Journal Article
• Authors: Brydges, Christopher R; Bielak, Allison A M
• Editors: Isaacowitz, Derek
• Is Part Of: Journals of gerontology. Volume 75:Number 1(2020)
• Page Start: 58
• Page End: 66
• Publication Date: 2019-03-16
• Subjects: Bayes factor -- Meta-research -- Null hypothesis significance testing -- Statistical power; Geriatrics -- Periodicals; Gerontology -- Periodicals; Aged -- Periodicals; Aging -- Periodicals; Psychology, Social -- Periodicals; 305.26
• DOI: 10.1093/geronb/gbz033
• Languages: English
• ISSNs: 1079-5014

Abstract

Abstract: Objectives: Nonsignificant p values derived from null hypothesis significance testing do not distinguish between true null effects or cases where the data are insensitive in distinguishing the hypotheses. This study aimed to investigate the prevalence of Bayesian analyses in gerontological psychology, a statistical technique that can distinguish between conclusive and inconclusive nonsignificant results, by using Bayes factors (BFs) to reanalyze nonsignificant results from published gerontological research. Methods: Nonsignificant results mentioned in abstracts of articles published in 2017 volumes of 10 top gerontological psychology journals were extracted ( N = 409) and categorized based on whether Bayesian analyses were conducted. BFs were calculated from nonsignificant t -tests within this sample to determine how frequently the null hypothesis was strongly supported. Results: Nonsignificant results were directly tested with BFs in 1.22% of studies. Bayesian reanalyses of 195 nonsignificant t -tests found that only 7.69% of the findings provided strong evidence in support of the null hypothesis. Conclusions: Bayesian analyses are rarely used in gerontological research, and a large proportion of null findings were deemed inconclusive when reanalyzed with BFs. Researchers are encouraged to use BFs to test the validity of nonsignificant results and ensure that sufficient sample sizes are used so that the meaningfulness of null findings can be evaluated.