statsr (version 0.3.0)

BF_app: Run the interactive Bayes Factor shiny app

Description

This app illustrates how changing the Z score and prior precision affects the Bayes Factor for testing H1 that the mean is zero versus H2 that the mean is not zero for data arising from a normal population. Lindley's paradox occurs for large sample sizes when the Bayes factor favors H1 even though the Z score is large or the p-value is small enough to reach statistical significance and the values of the sample mean do not reflex practical significance based on the prior distribution. Bartlett's paradox may occur when the prior precision goes to zero, leading to Bayes factors that favor H1 regardless of the data. A prior precision of one corresponds to the unit information prior.

Usage

BF_app()

Examples

# NOT RUN {
if (interactive()) { 
BF.app()
}
# }