Chi-squared test
A chi-square test is any statistical hypothesis test in which the test statistic has a chi-square distribution when the null hypothesis is true, or any in which the probability distribution of the test statistic (assuming the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough.
Specifically, a chi-square test for independence evaluates statistically significant differences between proportions for two or more groups in a data set.
- Pearson's chi-square test, also known as the Chi-square goodness-of-fit test
- Yates' chi-square test also known as Yates' correction for continuity
- Mantel-Haenszel chi-square test
- Linear-by-linear association chi-square test
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See also
- General likelihood-ratio tests, which are approximately chi-square tests
- McNemar's test, related to a chi-square test
- The Wald test, which can be evaluated against a chi-square distribution
External links
- Free tutorials from Six Sigmafirst
- Charles McCreery’s chi-square tutorial for Oxford University psychology students
- Chi-Square Calculator from GraphPad
- META STATISTICS PORTAL. Comparative international Statistics. Access to Data Sources around the Globe OECD Statistics, U.S. Data Sources, Census Bureau, White House, Eurostat, Penn World Tables, Groningen Development Centre Database, Economics Web Institute Stats, Pacific Exchange Rate Service etc.