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

Author

Listed:
  • Jae H. Kim

    (Independent researcher)

  • Philip I. Ji

    (Dongguk University Seoul)

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.

Suggested Citation

  • Jae H. Kim & Philip I. Ji, 2024. "Testing for signal-to-noise ratio in linear regression: a test under large or massive sample," Review of Managerial Science, Springer, vol. 18(10), pages 3007-3024, October.
    • Handle: RePEc:spr:rvmgts:v:18:y:2024:i:10:d:10.1007_s11846-023-00706-0
    DOI: 10.1007/s11846-023-00706-0
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    References listed on IDEAS

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    More about this item

    Keywords

    Effect size; Large sample size bias; Statistical inference; False positive;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G1 - Financial Economics - - General Financial Markets

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