As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive p-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.
What is the difference between the errors and the residuals ? What does it mean for a model to predict something ? What is a link function ? In the current post, we use four R functions (viz., the predict, fitted, residuals and simulate functions) to illustrate the mechanisms and assumptions of the general linear model.
This post aims to assess the average probability of participant presence in psychological experiments and, in the meantime, to introduce Bayesian logistic regression using R and the rethinking package.
In this post I will argue that the “match point situation” (i.e., the situation in which your entire life is dictated by chance only), is an everyday’ situation for the researcher who uses Null Hypothesis Significance Testing (NHST) without controlling power.