Data Science Day 5:
Chi-Square Application 2:
Test Independence of Two categorical variables, or known as Contingency Table.
We use the Chi-Square test of Independence to check if two categorical variables are independent, or have a strong association.
Example 1: Ice-cream Favor VS. Buyer’s Gender
We want to see if there is a preference for ice-cream favor based on the gender of people eating it
H0 (Null Hypothesis): The preference for Ice-Cream Favor and Buyer’s Gender are Independent, (There is no association between Ice-Cream Favor and Gender selection)
we will use SciPy package and chi2_contingency function in Python.
We see the p-value is 4.3e-08, which is significantly < 0.05. So we Reject the Null Hypotheses and Conclude the Ice-Cream Favor is dependent on the buyer’s Gender. Note: If if the total count is <5 the result might be biased, and if it is 2 x 2 table (2 categorical variables with 2 observations) we will proceed with Fisher’s Exact Test.
From the visualization, we can see the indeed there’s more girls prefer Green Tea favor Ice-cream. 🙂
To be continue….