The chi-square test is a widely used hypothesis testing method for count data, suitable for analyzing the association of categorical variables. Its fundamental concept lies in comparing the degree of fit or goodness of fit between the theoretical frequencies and the actual frequencies.

Operating Method

In [Statistical Analysis] – [Basic Statistics], click the “+” sign to create a new chart. Then, drag two categorical variables into the [Dimension] field and keep the [Quantity] field in the [Numerical Value] column. Check the box of “Chi-square” on the right to see the correlation between the two variables in the [Dimension] column.

The results of the chi-square test are displayed under the graph, including the chi-square value, degrees of freedom, P value, and significance.

Significance markers: P ≤ .05; P ≤ .01; P ≤ .001; and for other cases, display “not significant.” It indicates a considerable deviation between the observed and theoretical values if the p-value is very small, leading to the rejection of the null hypothesis, suggesting a significant difference between the data being compared. Otherwise, if the p-value is not small, the null hypothesis cannot be rejected, and thus, it cannot be concluded that there is a difference between the actual situation represented by the sample and the theoretical assumption.