We can use the f.ppf() function from the scipy.stats.f module to calculate the critical value for an F-test for a specific significance level in Python.
from scipy.stats import f # alpha = 0.05 dfn = 2 dfd = 57 alpha = 0.05 critical_value = f.ppf(1 - alpha, dfn, dfd) print("Critical value: ", critical_value)
Let’s say there are three distinct groups of students. Each group consists of 20 different students. Each group of students is subjected to a teaching method. And then, a test is conducted for each group of students. After the tests, the mean marks obtained by each group of students are compared. We perform a one-way ANOVA test to determine whether the difference in the mean marks obtained by each group of students is statistically significant. Here,
dfn = degrees of freedom while calculating the estimate of variance of numerator and dfd = degrees of freedom while calculating the estimate of variance of denominator
We know that in a one-way ANOVA test, dfn = (total number of groups – 1). And dfd = (total number of observations – total number of groups). So, in our case, dfn = (3 – 1) = 2 and dfd = (3×20 – 3) = 57.
Please note that the test statistic in a one-way ANOVA test follows the F-distribution. So, a one-way ANOVA test is also an F-test.
Also, the f.ppf() is the percent point function. The output of the above program will be like the following:
Critical value: 3.1588427192606465






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