How to fit reinforcement learning models to behavioral data using Bayesian inference. This part is focused on the hierarchical Bayesian modeling and particularly on the usage of hBayesDM package. Approaches for the model diagnostic, selection, validation are discussed. The post also goes over groups comparison using posterior distributions of model parameters. Additionally, a brief results comparison between Bayesian inference and Maximum Likelihood Estimation is provided.

Overview of the power and the effect size of the test and why low p-value is not always enough to make a decision.

Introduction to the inference for a population proportion using binomial test and $\chi^2$ test for independence.

Basic concept of the NHST and the idea of p-value. Overview of the inference for a population mean using one/two-sample $t$-tests and ANOVA.

Overview of how classification and regression trees work behind the scenes using the binary classification example.

Create interactive plots using plotly library in R.

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