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The t-test might be more useful in actual practice when standard deviation or variance is unknown.
What is a t-test?
The t-test is used to determine how the averages of various data sets differ from one another.
When variance is not provided, the T-test, a particular kind of parametric test, is used to determine how the means of two sets of data differ from one another. When variance is provided, the Z-test indicates a hypothesis test that determines whether the means of two datasets differ from one another.
Example of t-test: If you flip a coin 1,000 times, for instance, you can discover that the outcome is distributed normally over all trials.
Example of z-test: We are conducting research using information gathered from cohorts of students who have taken Elementary Statistics in the past.
T-tests are typically more suitable when addressing issues with small sample sizes, whereas z-tests are suitable for large sample sizes.
Using the t-test for an ordinal variable, their frequency is not even close to a normal distribution, and the arithmetic mean offers an unsuitable measure of location.
Because we frequently don't know the population standard deviation, Z-Tests have this drawback.
Learn more about t-tests here:
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