Goodness-of-fit tests like the Kolmogorov—Smirnov test can determine if a sample data set fits a specific distribution. This study found that algorithms used for continuous distributions can also be applied to discontinuous distributions to calculate the exact power and significance of these tests. This means we can accurately assess how well our data matches different types of distributions, even when they have breaks or gaps.