What are chisquare tests?
Statistics
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The chisquare test is a way to determine the odds for or against a given deviation from the expected statistical distribution. This somewhat complex statistical test computes the probability that there is no major difference between the expected frequency of an event with the observed frequency of that event—and especially to determine if the set of responses is significantly different from an expected set of responses only because of chance.
There are even various ways to perform this type of test, such as the most common type, called the Pearson’s chisquare test. Another is called the likelihood ratio chisquare test (or G test), in which a hypothesis is tested of no association of columns and rows in nominallevel tabular data. Yet another is the chisquare goodnessoffit test, which is just a different use of the Pearson chisquare; it is used to test if an observed distribution conforms to any other distribution. Taking this test one step further (making it more powerful) is the KolmogorovSmirnov goodnessoffit test, which is preferred for interval data.