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I have been studying hypothesis tests for a while now, endeavouring to expand my level of expertise in this area. One type of hypothesis test that I have recently studied is the difference in means hypothesis test.
A difference in means hypothesis test, also known as a two sample t-test, is a statistical method used to determine if there is a significant difference between the means of two independent samples. It is commonly used when the analyst wants to compare the average values of a certain variable between two distinct samples.
The difference in means hypothesis test is particularly useful in the following scenarios:-
- Compare two groups, such as comparing the heights of male and female athletes.
- Test treatment effects, such as comparing the grade point average of students who received additional tutoring against those who did not.
- Assess changes over time, such as comparing measurements before and after a certain intervention.
In order to demonstrate how the difference in means hypothesis test works, I have used Python to perform the test between the heights of male and female olympians. In order to achieve this, I have gone through a step by step procedure, as seen below:-
