The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

By John K. Kruschke, Torrin M. Liddell. Psychonomic Bulletin & Review, 2017

1,099 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/67e7b84a096e57f36768c985936ba82e33607f48

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Citations in the corpus

Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis (2018, background): “If a researcher plans a generic study design for a generic outcome, as in this case, the required sample size to obtain appropriate power may be 155 per group (LeBel et al. 2017).”
A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017, neutral): “…values in this way suffers two of the same problems as null hypothesis signiﬁ-cance testing using p-values: it can only reject but never accept a null value, and if used with optional stopping of data collection it will always reject null values even if they are true (Kruschke & Liddell, 2017).”
bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework (2019, neutral): “…testing of “effect existence”) by providing access to both established and exploratory indices of effect existence and significance (such as the ROPE, Kruschke & Liddell, 2018; the MAP-based p -value, Mills, 2018; the Bayes factor, Morey & Rouder, 2011, or the Probability of Direction - pd ).”
How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables (2019, methodology): “At this point, it is important to mention that proponents of the Bayesian approach are not overly enthusiastic about the yes/no null hypothesis significance testing discussed in the present article, for good reasons (e.g., Cumming, 2014; Gigerenzer & Marewski, 2015; Kruschke & Liddell, 2018).”
Visual working memory representation as a topological defined perceptual object. (2019, background): “…Bayesian approach over the frequentist approach is that the relative evidence favoring the null versus a specified alternative hypothesis is assessed, meaning the strength of the evidence favoring the null cannot be measured (Kruschke & Liddell, 2017; Rouder, Speckman, Sun, Morey, & Iverson, 2009).”
Rejecting or Accepting Parameter Values in Bayesian Estimation (2018, positive): “Meta-analysis is a central emphasis of the “New Statistics” endorsed by the journal Psychological Science (Cumming, 2014; Eich, 2014; Lindsay, 2015; Kruschke & Liddell, 2017b).”