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Getting oriented

This tutorial guides you through a Shiny app that puts frequentist and Bayesian analysis side by side.

This tutorial is best viewed in a wide window so the dynamic table of contents (TOC) appears on the left of the text. With the TOC visible, you can click in it to navigate to any section you like. In a narrow window, however, the TOC appears at the top of the screen and disappears when you scroll down.

Core structure of the app

The app is organized as a 2 \(\times\) 2 table: There is one column for frequentist analysis and a second column for Bayesian analysis; there is one row for estimation with uncertainty and a second row for null hypothesis tests. The cells of the 2 \(\times\) 2 table indicate the typical information provided by each type of analysis, as noted in the figure below:

The app's 2 x 2 table of analyses.

The app’s 2 x 2 table of analyses.

Why this structure?

The framework is explained in the article:
Kruschke, J. K. and Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25, 178-206. DOI:
This article will be referred to in this tutorial as The Bayesian New Statistics. Readers who are familiar with the layout of analyses in The Bayesian New Statistics will notice that the rows of the app are reversed relative to the table in that article. This reversal is intentional, to emphasize that hypothesis testing is not the default analysis and is not necessary to do at all.

Interactive sliders

The app has lots of interactive sliders with which you can specify aspects of the data and assumptions of the analyses, as suggested by the schematic figure below. The sliders are great for learning what happens to all of the analyses simultaneously. The sliders are also great for focusing what to think about when translating a real-world situation to analysis assumptions.

Schematic of sliders in the app.

Schematic of sliders in the app.

Learning objectives

The interactive sliders provide a natural framework for your learning objectives, and for how to assess your learning. If you have mastered the ideas, you should be able to predict the (qualitative) effect of every slider on every cell in the app, and be able to explain why the effect happens. Moreover, you should be able apply the analyses to the real world, which means you should be able to translate real-world information to appropriate settings of the sliders. These objectives are summarized in the figure below: