Abstract
In statistical practice the analysis of data is often reduced to making inference based on a simple and well known distribution such as the normal. Although the experimenter may feel that the underlying distribution from which the data is sampled may not be of the form postulated it may be close enough to allow useful conclusions for the purpose intended. On the other hand, the vagueness of how close or how far the true distribution may be from the one hypothesized, and how seriously this affects the reliability of conclusions based on the hypothetical distribution, may motivate a cautious experimenter to apply a test of fit.
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© 1975 D. Reidel Publishing Company, Dordrecht-Holland
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Gurland, J., Dahiya, R.C. (1975). Tests for Normality Using Minimum Chi — Square. In: Patil, G.P., Kotz, S., Ord, J.K. (eds) A Modern Course on Statistical Distributions in Scientific Work. NATO Advanced Study Institutes Series, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-1845-6_9
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DOI: https://doi.org/10.1007/978-94-010-1845-6_9
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