2018
DOI: 10.1093/geronb/gby065
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Improving Inferences About Null Effects With Bayes Factors and Equivalence Tests

Abstract: Researchers often conclude an effect is absent when a null-hypothesis significance test yields a non-significant p-value. However, it is neither logically nor statistically correct to conclude an effect is absent when a hypothesis test is not significant. We present two methods to evaluate the presence or absence of effects: Equivalence testing (based on frequentist statistics) and Bayes factors (based on Bayesian statistics). In four examples from the gerontology literature we illustrate different ways to spe… Show more

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Cited by 251 publications
(205 citation statements)
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“…In case of significant interactions, follow-up linear mixed models were used to explore their nature. In case of non-significant effects, Bayes factors were calculated as implemented in Version 25 of IBM SPSS Statistics to determine the likelihood of the data under the null hypothesis of no effect and Bayes factors lower than 1/3 were considered as support for the null model 48 .…”
Section: Cs Valence Ratingsmentioning
confidence: 99%
“…In case of significant interactions, follow-up linear mixed models were used to explore their nature. In case of non-significant effects, Bayes factors were calculated as implemented in Version 25 of IBM SPSS Statistics to determine the likelihood of the data under the null hypothesis of no effect and Bayes factors lower than 1/3 were considered as support for the null model 48 .…”
Section: Cs Valence Ratingsmentioning
confidence: 99%
“…This may not be the best approach, however, because it is likely that some effect sizes reported in previous research are inflated due to small sample sizes or publication bias (Ioannidis, 2008;Nuijten, van Assen, Veldkamp, & Wicherts, 2015;Szucs & Ioannidis, 2017). We therefore recommend future research to power experiments based on the smallest effect size of interest that is deemed theoretically or practically meaningful (see Lakens, McLatchie, Isager, Scheel, & Dienes, 2018;, and to utilise Bayesian analyses to make accurate statistical inferences (see Dienes, 2014;Lakens, McLatchie, et al, 2018). Moreover, we conducted a Bayesian meta-analysis, pooling the data from Experiment 1 and 2 to provide substantial support for the null hypothesis.…”
Section: Limitationsmentioning
confidence: 99%
“…Unlike NHST, Bayesian analyses do not rely on inferences based on statistical power and can show that a study unable to detect interesting effect sizes due to low statistical power provides evidence for the null hypothesis relative to the alternative hypothesis, or that a high-powered non-significant finding proffers no evidence for the null compared to the alternative hypothesis . We therefore recommend future research to power experiments based on the smallest effect size of interest that is deemed theoretically or practically meaningful (see Lakens, McLatchie, Isager, Scheel, & Dienes, 2018;, and to utilise Bayesian analyses to make accurate statistical inferences (see Dienes, 2014;Lakens, McLatchie, et al, 2018).…”
Section: Limitationsmentioning
confidence: 99%
“…However, future research should examine the strategies of guilty suspects under similar circumstances. Finally, although the Bayes factors observed (BF 01 = 3.90 for quantity of correct details and BF 01 = 7.82 for accuracy rates) suggest that the present findings likely reflect a genuine absence of the predicted effects of the interviewer's belief-led behaviour towards participants on their alibis (see Jeffreys, 1961;Lakens et al, 2018), we acknowledge the possibility of the study being underpowered. Thus, future research should attempt to replicate the present study using a larger sample size.…”
Section: Discussionmentioning
confidence: 62%