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Evidence and scientific research.

S N Goodman¹, R Royall

¹Department of Epidemiology, Johns Hopkins School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD 21205.

American Journal of Public Health

|December 1, 1988

Summary

This summary is machine-generated.

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P-values lack a measure of evidence, leading to confusion in statistical interpretation. Evidential theory, using the log-likelihood ratio, offers a clearer approach to quantifying evidence in scientific research.

Area of Science:

Statistics
Scientific Methodology

Background:

P-values are widely used in scientific research for hypothesis testing.
Debates persist regarding the appropriate interpretation and limitations of p-values.

Purpose of the Study:

To critically review the arguments surrounding the use of p-values.
To introduce evidential theory as a superior framework for quantifying evidence.

Main Methods:

Review of existing literature on p-value interpretation.
Presentation of the mathematical and logical foundations of evidential theory.
Utilizing the log-likelihood ratio as a measure of evidence.
Comparative analysis of traditional and evidential approaches.

Main Results:

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P-values lack a direct measure of evidence, contributing to misinterpretations.
Evidential theory provides a robust framework for quantifying evidence.
The log-likelihood ratio serves as an effective measure of evidence.

Conclusions:

The persistent confusion surrounding p-values stems from a lack of understanding of evidential principles.
Evidential theory offers a more philosophically sound and practically useful approach to statistical inference.
Adoption of evidential methods can enhance the clarity and rigor of scientific communication.