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167 – Business Analytics: The Role of Statistics

A Note on Quantifying Measure of Belief in a Significance Testing Problem

Sponsor: Section on Statistical Education
Keywords: Bayes factor, Likelihood Ratio, False Discovery Rate, P-value

Andrew Neath

SIU Edwardsville

Significance testing is commonly taught to introductory students as a data analytical tool for determining when a scientific hypothesis can be accepted as the true state of nature. Despite its popularity, however, the significance testing approach is ill-equipped for handling the problem of quantifying evidence. In this paper, we illustrate how the use of significance testing for providing a measure of belief in a hypothesis test result is contradictory to good scientific principles.

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