1965/09/01 by Malcolm J. Slakter · 2 citations
Business, Management and Accounting · Physics and Astronomy · Engineering · #Big Data and Business Intelligence #Statistical Mechanics and Entropy #Diverse Scientific and Engineering Research
paper · doi:10.2307/2283251
openalex publication_date 1965/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
This paper compares the Pearson Chi-Square and Kolmogorov good-ness-of-fit tests with respect to validity under the following conditions: (1) the N independent observations are tabulated and arranged into k mutually exclusive groups that are equally probable under the hypothesis to be tested; and (2) both N and k are “small”; i.e., not greater than 50. A random sampling experiment was performed, and the results show that in general for the conditions considered, the Pearson test is more valid than the Kolmogorov test.