When you conduct a Chi-Square test, you will get a test statistic as a result. To determine if the results of the Chi-Square test are statistically significant, you can compare the test statistic to a Chi-Square critical value. If the test statistic is greater than the Chi-Square critical value, then the results of the test are statistically significant.
The Chi-Square critical value can be found by using a Chi-Square distribution table or by using statistical software.
To find the Chi-Square critical value, you need:
- A significance level (common choices are 0.01, 0.05, and 0.10)
- Degrees of freedom
Using these two values, you can determine the Chi-Square value to be compared with the test statistic.
How to Find the Chi-Square Critical Value in Python
To find the Chi-Square critical value in Python, you can use the scipy.stats.chi2.ppf() function, which uses the following syntax:
- q: The significance level to use
- df: The degrees of freedom
This function returns the critical value from the Chi-Square distribution based on the significance level and degrees of freedom provided.
For example, suppose we would like to find the Chi-Square critical value for a significance level of 0.05 and degrees of freedom = 11.
import scipy.stats #find Chi-Square critical value scipy.stats.chi2.ppf(1-.05, df=11) 19.67514
The Chi-Square critical value for a significance level of 0.05 and degrees of freedom = 11 is 19.67514.
Thus, if we’re conducting some type of Chi-Square test then we can compare the Chi-Square test statistic to 19.67514. If the test statistic is greater than 19.67514, then the results of the test are statistically significant.
Note that smaller values of alpha will lead to larger Chi-Square critical values. For example, consider the Chi-Square critical value for a significance level of 0.01, and degrees of freedom = 11.
scipy.stats.chi2.ppf(1-.01, df=11) 24.72497
And consider the Chi-Square critical value with the exact same degrees of freedom, but with a significance level of 0.005:
scipy.stats.chi2.ppf(1-.005 df=11) 26.75685
Refer to the SciPy documentation for the exact details of the chi2.ppf() function.