Chi-Square Test of Independence: What’s the Straight Story
A chi-square test for your categorical variables can add some sugar and spice to your analysis and better inform your audience.
Chi-square tests of independence are used to test the hypothesis that two or more groups (or sets of observations) are related. The chi-square statistic is used to calculate the probability that the observed differences between groups are due to chance.
If the result of your test indicates that there is no significant relationship between the variables, then you can conclude that the variables are independent. If the test shows that there is a relationship, then the two variables may be dependent or correlated.
It complements other tests that rely mostly on quantitative variables such as linear models and t-tests and thus allow you to present a more complete analysis.
Is a university student more likely to be a science related major if he is a male? To perform a chi-square test in R to examine the relationship between the gender and a type of major (science vs. non-science), you will need to first organize your data into a contingency table. A contingency table is a table that displays the frequency or count of observations within two or more categorical variables.
# create a contingency table
major_table <…