Testing for signal-to-noise ratio in linear regression: a test under large or massive sample

Jae H. Kim () and Philip I. Ji ()
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Jae H. Kim: Independent researcher
Philip I. Ji: Dongguk University Seoul

Review of Managerial Science, 2024, vol. 18, issue 10, No 8, 3007-3024

Abstract: Abstract This paper proposes a test for the signal-to-noise ratio applicable to a range of significance tests and model diagnostics in a linear regression model. It is particularly useful when sample size is large or massive, where, as a consequence, conventional tests frequently lead to inappropriate rejection of the null hypothesis. The test is conducted in the context of the traditional F-test, with its critical values increasing with sample size. It maintains desirable size properties under a large or massive sample size, when the null hypothesis is violated by a practically negligible margin. The test is widely applicable to many empirical studies in business and management.

Keywords: Effect size; Large sample size bias; Statistical inference; False positive (search for similar items in EconPapers)
JEL-codes: C1 G1 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11846-023-00706-0

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