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The Kolmogorov-Smirnov Test for Goodness of Fit
Abstract
The test is based on the maximum difference between an empirical and a hypothetical cumulative distribution. Percentage points are tabled, and a lower bound to the power function is charted. Confidence limits for a cumulative distribution are described. Examples are given. Indications that the test is superior to the chi-square test are cited.
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“A Kolmogorov-Smirnov test [42] rejected the normality assumption”
From Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild (2016) · cites this paper as [42]
Citations
Cites
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Non-parametric Statistical Inference
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Metadata
- Authors
- Massey, F. J.
- Affiliations
- University of Oregon (US)
- Corresponding author
- Massey, Frank J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1951-03
- Volume
- 46
- Issue
- 253
- Pages
- 68-78
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W4255375128
- Cited by
- 7,210
- References
- 12
- Citations by year
- 2026: 529 · 2025: 691 · 2024: 580 · 2023: 591 · 2022: 576 · 2021: 562 · 2020: 490 · 2019: 432 · 2018: 369 · 2017: 351 · 2016: 285 · 2015: 273 · 2014: 215 · 2013: 226 · 2012: 181
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- Kolmogorov-Smirnov test, goodness of fit, empirical distribution function, cumulative distribution function, percentage points, power function, confidence limits
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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