Optimal Level of Significance for Regression and Other
Statistical Tests
Description
The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model.
The details are covered in Kim and Choi (2020) , and Kim (2021) .