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Comment on Murtaugh.

Michael Lavine

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    Summary
    This summary is machine-generated.

    Abandoning accept/reject declarations is key to improving statistical practice. Statistical measures like P values and deltaAIC should not be treated as binary outcomes, and their interpretation requires careful recalibration for each problem. Plotting is essential for model checking.

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    Area of Science:

    • Statistics
    • Scientific Methodology

    Background:

    • Traditional statistical practices often rely on binary accept/reject decisions.
    • P-values and deltaAIC are frequently misinterpreted as definitive measures of evidence.
    • This can lead to flawed conclusions and hinder scientific progress.

    Purpose of the Study:

    • To advocate for the abandonment of accept/reject declarations in statistical practice.
    • To clarify the appropriate interpretation of P-values and deltaAIC.
    • To emphasize the importance of data visualization and model checking.

    Main Methods:

    • Conceptual analysis and critique of current statistical reporting standards.
    • Discussion on the limitations of binary hypothesis testing.
    • Recommendations for improved statistical interpretation and practice.

    Main Results:

    • Abandoning accept/reject declarations can significantly enhance statistical practice.
    • P-values and deltaAIC should not be conflated with binary outcomes.
    • The interpretation of P-values and deltaAIC as strength of evidence is context-dependent and requires recalibration.

    Conclusions:

    • Statistical significance should not be reduced to a binary decision.
    • Researchers must exercise caution when interpreting P-values and deltaAIC.
    • Embracing data visualization and thorough model checking are crucial for robust statistical analysis.