Resource Library

This page supports an in-class exercise that highlights several key Bayesian concepts. The scenario is as follows: a large paper bag contains pieces of candy with wrappings of different color, and we are interested in learning about the unknown proportion of yellow-wrapped pieces of candy. After completing the exercises, we will be familiar with the following concepts and ideas: probability distributions can quantify degree of belief, prior distribution, posterior distribution, sequential updating, conjugacy, Cromwell’s Rule (http://en.wikipedia.org/wiki/Cromwell's_rule), the data overwhelm the prior, Bayes factors, Savage-Dickey density ratio, sensitivity analysis, coherence.

Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

Check how your Bayes factor conclusion depends on the r-scale parameter.

This Shiny app implements the p-curve (Simonsohn, Nelson, & Simmons, 2014; see http://www.p-curve.com) in its previous ("app2") and the current version ("app3"), the R-Index and the Test of Insufficient Variance, TIVA (Schimmack, 2014; see http://www.r-index.org/), and tests whether p values are reported correctly.

When does a significant p-value indicate a true effect?  This app will help with understanding the Positive Predictive Value (PPV) of a p-value.

This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124

Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?

Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.

Use presets or change parameter values manually to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries. For detailed explanation and conceptual background, see LeBel, Campbell, & Loving (in press, JPSP), Table 3. This app is an extension of Zehetleitner and Felix Schönbrodt's (2016) positive predictive value app. 

This app allows you to derive an approximation to the difference in Bayesian information criterion and to the probability of the null and the alternative hypothesis from the sum of squares obtained in an ANOVA analysis.

Required input

• Number of participants
• Df ... degrees of freedom of the effect of interest
• Whether the effect is between or within participants
• SSEffect ... sum of squares of the effect of interest
• SSError ... sum of squares of the error, for within-factors the by-subject error, associated with this effect
• SSTotal ... total sum of squares, only required for within-participant designs when using effective sample size (strongly recommended, Nathoo & Masson, 2007)