In previous posts, we have seen different types of tests that we can use to analyze our data and test hypotheses.
The chi-square test was proposed by Karl Pearson in 1900, and it is widely used to estimate how effectively the distribution of a categorical variable represents an expected distribution (in this case, we talk about the “Goodness of Fit Test”) or to estimate when two categorical variables are independent of each other (and then we talk about the “Test of Independence”).
Such is the importance and widespread use of this test that it was listed by the magazine Scientific American among the 20 most important scientific discoveries of the 20th century.
Continue reading “The Chi-Square Test: Goodness of Fit and Test of Independence”