These errors are called Type I and Type II errors respectively and are important to the process of hypothesis testing. Type I Error: Reject a null hypothesis when it is true. Type II Error: Fail to ...

This repository contains code and results for a simulation study examining the performance of Poisson regression models under violations of the equi-dispersion assumption (mean = variance).

We provide the first analysis of racial in-group bias in Type-I and Type-II errors. Using player-referee matched data from NBA games we show that there is no overall racial bias or in-group bias in ...

The applet displays the null distribution by default. The user can choose to display the alternative distribution as well and can change the values of μ 0, μ 1, and σ using the sliders. By selecting ...