We can use the scipy.stats module to calculate the critical value for a t-test using Python. The critical value for a t-test depends on a few factors:
- The degrees of freedom
- Whether the test is a left-tailed, right-tailed, or two-tailed t-test
- The significance level or alpha
We can use the following Python code to calculate the critical value for a left-tailed, right-tailed or two-tailed t-test for a specific degrees of freedom and significance level.
from scipy import stats degrees_of_freedom = 19 alpha = 0.05 # Calculate critical value for left-tailed t-test for alpha = 0.05 critical_value_left_tailed = stats.t.ppf(q=alpha, df=degrees_of_freedom) print("Critical value for left-tailed t-test: ", critical_value_left_tailed) # Calculate critical value for right-tailed t-test for alpha = 0.05 critical_value_right_tailed = stats.t.ppf(q=1 - alpha, df=degrees_of_freedom) print("Critical value for right-tailed t-test: ", critical_value_right_tailed) # Calculate critical value for two-tailed t-test for alpha = 0.05 critical_value_two_tailed = stats.t.ppf(q=1 - alpha/2, df=degrees_of_freedom) print("Critical-left value for two-tailed t-test: ", critical_value_two_tailed)
Here, we are using the t.ppf() function from the scipy.stats module to calculate the critical value for specific degrees of freedom and alpha. The t.ppf() function is a percent point function.
The output of the above program will be like the following:
Critical value for left-tailed t-test: -1.7291328115213678 Critical value for right-tailed t-test: 1.729132811521367 Critical-left value for two-tailed t-test: 2.093024054408263






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