Chi-Square Independence Test

A Chi-Square Independence Test is a statistical independence test based on the chi-square distribution.

• See: Chi-Square Goodness-of-Fit Test.

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References

2004

• http://www.quality-control-plan.com/StatGuide/sg_glos.htm
  • QUOTE: Pearson's chi-square test for independence for a contingency table tests the [>null hypothesis</a> that the row classification factor and the column classification factor are independent. Like the chi-square goodness-of-fit test, the chi-square test for independence compares observed and expected frequencies (counts). The expected frequencies are calculated by assuming the null hypothesis is true. The chi-square test statistic is basically the sum of the squares of the differences between the observed and expected frequencies, with each squared difference divided by the corresponding expected frequency. Note that the chi-square statistic is always calculated using the counted frequencies. It can not be calculated using the observed proportions, unless the total number of subjects (and thus the frequencies) is also known.

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