The Chi-Square analysis is a procedure used by statistical researchers to evaluate differences in variables of a given set of population. The test can be used to test independent variables in population data (Anderson et al., 2017, p. 521). The analysis is also used to check for a specific probability distribution in a given population; this is referred to as a goodness of fit test (Anderson et al., 2017, p. 529). This paper will discuss how the important test is used in the two procedures.
In the test of independence application, the Chi-Square analysis or the Chi-Square test of association is used to determine whether categorical variables are associated; this may lead to the conclusion that the variables are related or independent (Anderson et al., 2017, p. 521). In these tests, researchers develop a null and alternative hypothesis for the cases. An example is a test for independence or the relationship between the perception of a charity program administrative expenses and the occupation of the respondent (Anderson et al., 2017, p. 510). It is important to note that the test only works for categorical variables; meaning the variables are fixed and not continuous, or a mixture of categorical and continuous variables.
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The second application of the Chi-Square analysis is the goodness of fit test where the test is used to check for a specific probability distribution in a given population; the test determines how the observed value in given research differs from the expected value (Anderson et al., 2017, p. 529). The analysis finds out how theoretical distributions are relevant to the empirical distribution (Anderson et al., 2017, p. 529). Just like the test of independence, a null and an alternative hypothesis are used in this procedure.
Reference
Anderson, D., Sweeney, D., Williams, T., & Cochran, J. (2017). Statistics for Business & Economics (pp. 521-530). South Western Educational Publishing.
