Abstract
The Berk-Jones test and the reversed Berk-Jones test are shown to be biased by computing the exact minimum power with confidence bands for the continuous distribution function. The bias correction is applied to the Berk-Jones test and the reversed Berk-Jones test by using a similar process of Frey (Citation2009). In order to compose the unbiased test, various critical values are listed. Simulations are used to compare the power of the biased and unbiased Berk-Jones and reversed Berk-Jones tests for various population distributions. Numerical results indicate that the unbiased Berk-Jones test is more powerful than the unbiased reversed Berk-Jones test.
Acknowledgments
The author would like to thank the editor and the referee for their valuable comments and suggestions.