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Why a P-Value is Not Enough.

Federico Solla¹, Antoine Tran², Domenico Bertoncelli³

¹Department of Orthopaedic Surgery.

Clinical Spine Surgery

|July 24, 2018

Summary

This summary is machine-generated.

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Statistical significance requires more than a P-value. Reporting effect sizes and confidence intervals (CI) alongside P-values provides a more complete analysis of scientific data.

Area of Science:

Biostatistics
Medical Research
Scientific Publication

Background:

P-values are commonly used to determine statistical significance (P<0.05).
However, P-values alone do not fully represent the magnitude or clinical relevance of findings.
Publications often require additional statistical parameters for comprehensive data analysis.

Purpose of the Study:

To present P-values, confidence intervals (CI), and common effect sizes (e.g., Cohen's d, odds ratio) in a simplified manner.
To emphasize the importance of these additional parameters for robust scientific interpretation.

Main Methods:

Explanation of P-value interpretation: probability of results occurring by chance.
Definition and interpretation of confidence intervals (CI), including the significance threshold.

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Introduction to effect sizes as quantitative measures of association strength.

Main Results:

P-values indicate statistical significance but not the size of an effect.
Confidence intervals provide a range for the true value, with significance indicated by exclusion of the null hypothesis.
Low effect sizes, even with P<0.05, may lack clinical significance.

Conclusions:

Scientific publications necessitate reporting of effect sizes and CIs beyond P-values.
Including these parameters ensures a more complete, honest, and useful interpretation of research findings.
This approach enhances the overall quality and impact of scientific communication.