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On the Appropriate Size of Samples in chi[sup 2]Tests: A Reply to Kottas and Lau.

Jack C. Hayya¹; William L. Ferrara²; Erwin M. Saniga³

¹ Professor of Management Science, Pennsylvania State University 1 · ² Professor of Accounting, Pennsylvania State University 2 · ³ Assistant Professor of Management Science, University of Delaware 3

The article presents a reply to authors John F. Kottas and Hon-Shiang Lau, on the appropriate size of samples in &chi 2 tests. The paper by FHN falls into the realm of accounting risk analysis. In risk analysis, it attempt to incorporate uncertainty into the decision process and, at the same time, try to make the analysis as simple as possible so that it can be understood by the manager and so that it can be done by those with elementary knowledge in statistics and mathematics. There is, therefore, a need in risk analysis to reduce intractible functions to reasonable approximations, preferably to the normal approximation as it is the most familiar and easiest distribution for the practitioner to use. In simulation experiments, the record has been that the choice of sample size was, for the most part, arbitrary. Better yet would be a choice based on the fulfillment of some important criteria of validity, for example, that the minimum expected frequency in a cell be no less than one in a chi-square goodness-of-fit test.

DOI

10.2308/tar-4500850

Volume

53 (1)

Pages

252-259

Language

en

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BibTeX

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