An Analysis of Nonsignificant Results in Audiology Using Bayes Factors

Purpose: Null hypothesis significance testing is commonly used in audiology research to determine the presence of an effect. Knowledge of study outcomes, including nonsignificant findings, is important for evidence-based practice. Nonsignificant "p" values obtained from null hypothesis significance testing cannot differentiate between true null effects or underpowered studies. Bayes factors (BFs) are a statistical technique that can distinguish between conclusive and inconclusive nonsignificant results, and quantify the strength of evidence in favor of 1 hypothesis over another. This study aimed to investigate the prevalence of BFs in nonsignificant results in audiology research and the strength of evidence in favor of the null hypothesis in these results. Method: Nonsignificant results mentioned in abstracts of articles published in 2018 volumes of 4 prominent audiology journals were extracted (N = 108) and categorized based on whether BFs were calculated. BFs were calculated from nonsignificant "t" tests within this sample to determine how frequently the null hypothesis was strongly supported. Results: Nonsignificant results were not directly tested with BFs in any study. Bayesian re-analysis of 93 nonsignificant "t" tests found that only 40.86% of findings provided moderate evidence in favor of the null hypothesis, and none provided strong evidence. Conclusion: BFs are underutilized in audiology research, and a large proportion of null findings were deemed inconclusive when re-analyzed with BFs. Researchers are encouraged to use BFs to test the validity and strength of evidence of nonsignificant results and ensure that sufficient sample sizes are used so that conclusive findings (significant or not) are observed more frequently.