The Chi Square test is used to investigate if a relationship between categorical data exists. Categorical variables are variables with no intrinsic ordering to the categories and the ranking does not matter. Examples of categorical variables include hair color, gender, race, and nationality, as opposed to numerical variables such as height, weight, or income.
Chi Square outputs with very small p-value, less than significance level 0.05, mean that relationship between two categorical variables exists.
In Report Example 3, Step 9, an alpha value of 05 was selected and results are shown in the following Chi Square Testing Results window.
In the example above, the p-value is below the significance level of 0.05 so the Chi Square statistic is significant. This means you reject the null hypotheses. Based on the rejection, you can conclude that the column variable Model Type (Hybrid/Non-Hybrid) is influenced by the row variable TME (Survey Count by Region) and association exists. Therefore both variables are not independent of each other.
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