![]() ![]() For example, consider the Chi-Square critical value for a significance level of 0.01, and degrees of freedom = 11. ![]() Note that smaller values of alpha will lead to larger Chi-Square critical values. 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. The Chi-Square critical value for a significance level of 0.05 and degrees of freedom = 11 is 19.67514. This function returns the critical value from the Chi-Square distribution based on the significance level and degrees of freedom provided.įor example, suppose we would like to find the Chi-Square critical value for a significance level of 0.05 and degrees of freedom = 11. To find the Chi-Square critical value in Python, you can use the () function, which uses the following syntax: How to Find the Chi-Square Critical Value in Python Using these two values, you can determine the Chi-Square value to be compared with the test statistic. A significance level (common choices are 0.01, 0.05, and 0.10).To find the Chi-Square critical value, you need: The Chi-Square critical value can be found by using a Chi-Square distribution table or by using statistical software. If the test statistic is greater than the Chi-Square critical value, then the results of the test are statistically significant. 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. When you conduct a Chi-Square test, you will get a test statistic as a result. ![]()
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