Bayesian estimation supersedes the t test.

By John K. Kruschke. Journal of experimental psychology. General, 2013

1,517 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dea60927efbd1f284b4132eae3461ea7ce0fb62a

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Citations in the corpus

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research (2013, positive): “For a more detailed introduction, see Kruschke (2011b, 2013), and for a more technical introduction, see Lynch (2007) or Gelman et al. (2004).”
Rejecting or Accepting Parameter Values in Bayesian Estimation (2018, background): “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).”
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis (2016, methodology): “ty. The high-level distribution of the ˙ i’s is unif(0;˙), as recommended by Gelman (2006), as it yields inferences which are insensitive to ˙, if ˙is large enough. To this end we set ˙= 1000 s (Kruschke, 2013), where s= P q i ˙^ i=q. We complete the model with the prior on the parameters 0, ˙ 0 an”
Using Bayes to get the most out of non-significant results (2014, methodology): “Update the prior to obtain the posterior distribution (see e.g., Kruschke, 2013b, or tools on the website for Dienes, 2008: http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/bayes_normalposte rior.swf).”
Is psychology suffering from a replication crisis? What does "failure to replicate" really mean? (2015, methodology): “It should also be noted that Equation 2 is developed from a frequentist perspective, but Kruschke (2013, 2014) describes a Bayesian sample size approach based on the ROPE.”
Best practice guidance for linear mixed-effects models in psychological science (2019, background): “An increasing number of researchers advocate the adoption of Bayesian analysis methods (Kruschke, 2013; McElreath, 2015) in which estimates for fixed effects coefficients and random effects variances (or covariances) are associated with posterior distributions that allocate varying probabilities to ”
Some Natural Solutions to the p-Value Communication Problem—and Why They Won’t Work (2017, methodology): “The general idea is to use Bayesian or regularized inference as a replacement of hypothesis tests but in the manner of Kruschke (2013), through estimation of continuous parameters rather than by trying to assess the probability of a point null hypothesis.”
Bayesian alternatives to null-hypothesis significance testing for repeated-measures designs (2016, methodology): “For simple t test designs, the method described by Kruschke (2013) continues to be a very attractive option (http://www.indiana.edu/kruschke/ BEST/) as it provides a complete Bayesian analysis including posterior density distributions and highest density intervals.”
Sensor data quality: a systematic review (2020, methodology): “the probability of every possible difference of means and every possible difference of standard deviations which allows the estimation of the difference between the two groups rather than simply testing whether the two groups are different based on the observed data [104].”
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective (2017, positive): “Other introductory resources include a companion article in this issue (Kruschke & Liddell, 2015), Chapter 2 of Kruschke (2015, available free online), and other articles (e.g., Kruschke, 2013; Kruschke, Aguinis, & Joo, 2012).”
Agronomic Practices for Reducing Wheat Yield Gaps: A Quantitative Appraisal of Progressive Producers (2019, positive): “…compared the weather between the highest- vs. lowest-yielding groups of fields determined as yield terciles in the dataset (n = 33; Wiese, 1982a) using posterior Bayesian estimates for group means and SDs (Kruschke, 2013) using the ‘BEST’ package in the R software (R Development Core Team, 2013).”
Bayesian Analysis of Factorial Designs (2017, background): “The fact that Bayes factors are dependent on prior settings has fueled the critique that they are too subjective (e.g., Gelman & Shalizi, 2013; Kruschke, 2013; Liu & Aitkin, 2008).”
Sample Size Justification (2021, positive): “, 2018), and an increasing number of applied tutorials for specific tests (Brysbaert & Stevens, 2018; DeBruine & Barr, 2019; P. Green & MacLeod, 2016; Kruschke, 2013; Lakens & Caldwell, 2021; Schoemann et al., 2017; Westfall et al., 2014).”
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2014, background): “These cases have been discussed many times in the literature, including the well-known and accessible articles by Lindley and Phillips (1976) and J.”