Chi-square test
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A chi-square test is any statistical hypothesis test in which the test statistic has a chi-square distribution if the null hypothesis is true. These include:
- Pearson's chi-square test, the original and most widely used chi-squared test
- General likelihood-ratio tests are approximately chi-square tests when the sample-size is large. They are widely used in logistic regression. However, in cases where the exact distribution of the likelihood-ratio statistic can be easily calculated e.g., F-tests in the analysis of variance and t-tests are likelihood-ratio tests, it is more appropriate to refer to use these exact statistics.
- Yates' chi-square test, or Yates' correction for continuity
- Mantel-Haenszel chi-square test
- linear-by-linear association chi-square test
- McNemar's test
The chi-square test is a statistical tool to separate real effects from random variation. It can be used on data that is:
- randomly drawn from the population
- reported in raw counts of frequency (not percentages or rates)
- measured variables must be independent
- values on independent and dependent variables must be mutually exclusive
- observed frequencies cannot be too small
The chi-square test determines the probability of obtaining the observed results by chance, under a specific hypothesis. It tests independence as well as goodness of fit for a set of data.de:Chi-Quadrat-Test fr:Test du χ² it:Test chi quadrato ja:カイ二乗検定 lv:Hī kvadrāta kritērijs nl:Chi-kwadraattoets pl:Test zgodności chi-kwadrat su:Tes chi-kuadrat vi:Kiểm định chi-bình phương