Chi-Square test

Statistics
Major approaches
Frequency probability
Bayesian inference
Non-parametric statistics
Common methods
Analysis of variance
Chi-Square test
Students t-test
Z test
Linear regression
Bayesian model selection
Bootstrapping

The Chi-square test is a statistical test that relies on the Chi-square distribution. The chi-square test is non-parametric and does not make as many assumptions about the data it is comparing. However, it has less statistical power because of this. The most common usage for the chi-square test is to compare statistical significance of the difference between proportions in data sets. This usually takes the form of a Bivariate tabular analysis, or the intersections of proportional data of an independent variable and a dependent variable.

For example, ones independent variable might be political affiliation and the dependent variable might be support for a particular law. In this hypothetical example the data looks like:

	Oppose	Support
Liberal	45	5
Conservative	10	40

A chi-square test would be used to answer whether or not the difference between the relative proportions of support and opposition for the law compared to political affiliation was significant.