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Related Experiment Videos

No adjustments are needed for multiple comparisons.

K J Rothman

    Epidemiology (Cambridge, Mass.)
    |January 1, 1990
    PubMed
    Summary
    This summary is machine-generated.

    Adjusting for multiple comparisons in large datasets increases false negatives. Not adjusting multiple comparisons is preferable for empirical research, preventing missed scientific findings.

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    Area of Science:

    • Statistical methodology
    • Scientific research integrity

    Background:

    • Multiple comparison adjustments are standard practice to control Type I errors.
    • However, these adjustments can inflate Type II errors, masking true associations.

    Purpose of the Study:

    • To evaluate the implications of routine multiple comparison adjustments in empirical research.
    • To propose an alternative approach prioritizing the discovery of genuine scientific findings.

    Main Methods:

    • Conceptual analysis of statistical hypothesis testing.
    • Examination of the "universal null hypothesis" and its impact on empirical research.

    Main Results:

    • Routine adjustments for multiple comparisons are based on a "universal null hypothesis" that contradicts empirical research principles.

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  • Failing to adjust for multiple comparisons leads to fewer interpretation errors with real-world data.

    • Conclusions:

    • A policy of not adjusting for multiple comparisons is recommended for empirical research.
    • This approach minimizes missed discoveries and avoids penalizing scientists for exploring potentially significant leads.