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Chi-square test

The Chi-square test, also known as Pearson’s Chi-square test or the goodness-of-fit test, is a statistical method used to determine whether there is an association between two variables. It compares observed frequencies with expected frequencies in one or more categories of a variable. It is primarily used in experiments that involve categorical data and can be used to compare different distributions of categorical data to determine if they are related.

Working  and Applications of Chi-square Test

The chi-square test uses a chi-square statistic, which is calculated by subtracting the expected frequency from the observed frequency and then squaring it, dividing the result by the expected frequency and then adding up all of these values. This results in a number that indicates how closely the two variables correlate. The higher this number is, the greater difference there is between expected frequencies and observed frequencies, indicating that there may be some correlation between the two variables being studied. In addition to determining if two variables are correlated, the chi-square test can also help identify patterns within categorical data.

It can help answer questions such as “Is there a relationship between gender and political party?” If so, what kind? The chi-square test can also be used to compare different groups within one variable to each other (for example: “Are people born before or after 1990 more likely to vote for certain political parties?”). It should be noted that while useful in certain circumstances, the chi-square test does have limitations. In particular, this method does not take into account any form of sampling bias that may exist when studying a population – for example, if individuals who are easy to reach are overrepresented in a sample due to their availability (e.g., responding repeatedly to surveys), this could lead to inaccurate conclusions when using this method of analysis. Additionally, since this method works best on large datasets with many observations (hundreds or more), it can be difficult to use on smaller samples like those typically seen in social science research studies.