Chi-Squared (X2) Statistical Test in R

This chapter explains and practically illustrates to the readers how to apply a Chi-squaredChi-squared (X2) analysis in R. The Chi-squared test is used to compare how expectationsExpectations are linked or related with the actual observedObserved (frequency, fact, behaviour, relationship, fitting, distribution) datasets or experimental data. Two main types of tests or analyses are usually applied by the researchers using the Chi-squared analysis. This includes the (i) Independence testIndependence test which is defined as a test of “relationship” that allows the researcher or data analyst to compare two (categorical) variables to determine whether they are related or not, and (ii) Goodness of fitGoodness of fit test which allows the user to determine whether a proportion of a data sample matches the larger population. Thus, if the analyzed data does not match or fit the assumed (expected) characteristics of the intended population, usually determined through the p-value (p ≤ 0.05)P-value, then the users or researcher may not consequentially want to use the drawn data or sample to make any conclusion about the studied (larger) population in question. The content of this chapter also covers how to graphically visualize the data relationships and interpretation of the result of the Chi-squared (X2) analysis in R.