Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison

By John K. Kruschke. Perspectives on Psychological Science, 2011

437 citations recorded by the source corpus.

Subfields: Food Science, General Decision Sciences, Social Psychology

View Paper: https://www.semanticscholar.org/paper/2d267fd3bbfd60696af521f0bf7c5333b2bd5faf

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Citations in the corpus

How Bayesian statistics are needed to determine whether mental states are unconscious (2015, neutral): “In general, B indicates how much more probable the data are on one theory (say H1, the alternative hypothesis, your pet theory) rather than on another theory (say H0, the null hypothesis); thus it measures the amount of evidence data provide for H1 compared to H0 (e.g. Jeffreys 1939/1961; Berger and”
MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis (2012, positive): “…posterior probabilities for H 0 versus H 1 is poised to emerge as a widely used alternative to NHST in psychological research (e.g., Dienes, 2011; Kruschke, 2011; Masson, 2011; Raftery, 1995; Wagenmakers, 2007; Wetzels et al., 2011; see also Dixon, 2003; Glover & Dixon, 2004, for related…”
Is psychology suffering from a replication crisis? What does "failure to replicate" really mean? (2015, background): “However, proponents such as Rouder, Speckman, Sun, Morey, and Iverson (2009) see this as a plus, whereas others such as Kruschke (2011) suggest it can sometimes be a disadvantage.”
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research (2013, neutral): “As stated by Kruschke (2011a), in a special issue of Perspectives on Psychological Science : whereas the 20th century was dominated by NHST [null hypothesis signi ﬁ cance testing], the 21st century is becoming Bayesian.”
Sample Size Justification (2021, neutral): “prior would compute a credible interval with the same (or a very similar) upper and lower bound (Albers et al., 2018; Kruschke, 2011), and might conclude that after collecting”
Bayesian Versus Orthodox Statistics: Which Side Are You On? (2011, methodology): “(Note that I will not discuss confidence intervals and credibility intervals, the Bayesian equivalent to a confidence interval, in this article; see Dienes, 2008, and Kruschke 2010b, 2011, for detailed discussion and calculations.)”
Rejecting or Accepting Parameter Values in Bayesian Estimation (2018, neutral): “This HDI+ROPE decision rule has been described in several previous publications (Kruschke, 2010, 2011a, 2011b, 2013, 2015; Kruschke, Aguinis, & Joo, 2012; Kruschke & Liddell, 2018a, 2018b; Kruschke & Vanpaemel, 2015).”
Bayesian Analysis of Factorial Designs (2017, background): “Interested readers are referred to Berger and Berry (1988), Edwards, Lindman, and Savage (1963), Efron (2005), Gelman, Shor, Bafumi, and Park (2007), Jaynes (2003), Kruschke (2011), and Wagenmakers (2007) among others.”
Indices of Effect Existence and Significance in the Bayesian Framework (2019, background): “…investigate the robustness of these indices to sampling characteristics (e.g., sampling algorithm, number of iterations, chains, warm-up) and the impact of prior specification (Kass and Raftery, 1995; Vanpaemel, 2010; Kruschke, 2011), all of which are important parameters of Bayesian statistics.”
The Rules of the Game Called Psychological Science (2012, methodology): “Notwithstanding the criticism it has received (Cohen, 1990, 1994; Gigerenzer, 2004; Kruschke, 2011; Meehl, 1978; Nickerson, 2000; Rozeboom, 1960; Wagenmakers, 2007; Wetzels et al., 2011), NHST continues to be the main method of statistical inference in many fields.”
Bayesian estimation supersedes the t test. (2013, methodology): “pdf Kruschke, J. K. (2010a). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 658–676. doi:10.1002/wcs.72 Kruschke, J. K. (2010b). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14, 293–300. doi:10.1016/ j.tics.2010.05.001 Kruschke, J”
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective (2017, positive): “(Bayes factors for the individual betablocker studies can be computed exactly using extensions of formulas described on pages 160 and 305 of Kruschke, 2011b.) What should be the publication status of indecisive studies?”
Using Bayes to get the most out of non-significant results (2014, background): “For that assertion to be relevant to a given scientific context, the minimal value must be relevant to that scientific context (i.e., it cannot be determined by properties of the data alone nor can it be a generic default).”
Bayesian Estimation and Inference (2015, background): “In any case, because of the focus on rejecting nulls with frequentist methods, theorizing null effects is almost never done, even though hypothesizing nulls can be useful (Greenwald, 1975; Kruschke, 2011; Schwab et al., 2011).”
Default Bayes Factors for Model Selection in Regression (2012, background): “Kruschke (2011), for example, recommends plotting posteriors of effects and checking whether they cover null values.”
What cognitive processes drive response biases? A diffusion model analysis (2011, result): “…to disambiguate model parameter influence, rather than the commonly used 95% confidence level for experimental effects, because of the growing concern that null hypothesis significance tests using p = .05 may not lead to strongly supported conclusions (e.g., Kruschke, 2011; Wetzel et al., 2011).”