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The Chi-Square Test

Author(s): Lou Gross1, Monica Beals1, Susan Harrell1

University of Tennessee Knoxville

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Summary:
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.

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

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

Keywords

  1. Probability
  2. Probability & Statistics
  3. Genetics and Evolution
  4. Statistical Testing

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Description

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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