A chi-square test tells one how likely it is that a set of numbers one has could have been generated by random assignment of numbers. It is used to help build arguments that a given set of numbers (usually counts along two dimensions) does or does not arise from real differences in the world.
For example, one might wish to test if men of a given age and in a given socioeconomic milieu are more likely than women of the same age and socioeconomic milieu to buy a portable music player. You could ask all members of the group if they had bought portable music players. Your results might look like this (N.B.: invented data):
Bought a player Did not buy a player
Men 15342 25774
Women 17994 23164
A chi-square test could tell you how likely it is that being a woman makes one more likely to buy a portable music player. (Note further that the categories used in chi-square tests should be (a) "natural" categories and (b) should be exhaustive.
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The Chi-squared statistic can be used to test for association.
A chi-squared test is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true.
(r-1)x(c-1)
There are many chi-squared tests. You may mean the chi-square goodness-of-fit test or chi-square test for independence. Here is what they are used for.A test of goodness of fit establishes if an observed frequency differs from a theoretical distribution.A test of independence looks at whether paired observations on two variables, expressed in a contingency table, are independent of each.
Yes, Chis squared test are among the most common nonparametric statistics tests.