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The Chi-Square Test

By Lou Gross1, Monica Beals1, Susan Harrell1

University of Tennessee Knoxville

This module introduces how to determine whether observation is significantly different from expectation in the context of understanding Chi-square Test. It is intended for an introductory biology audience.

Listed in Teaching Materials | resource by group Quantitative Biology at Community Colleges

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Version 1.0 - published on 15 Feb 2019 doi:10.25334/Q4SB2P - cite this

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms


Student Introduction: The chi-square test is a statistical test that can be used to determine whether observed frequencies are significantly different from expected frequencies. For example, after we calculated expected frequencies for different allozymes in the HARDY-WEINBERG module we would use a chi-square test to compare the observed and expected frequencies and determine whether there is a statistically significant difference between the two. As in other statistical tests, we begin by stating a null hypothesis (H0: there is no significant difference between observed and expected frequencies) and an alternative hypothesis (H1: there is a significant difference). Based on the outcome of the chi-square test we will either reject or fail to reject the null hypothesis.


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