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T-Test

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T-Test

A T-test is used to determine the difference between at-least two groups of data in order to test if they came from the same population. When a research study is completed, there will be lots of data but it will be important to assess whether the data is reliable. A T-test is used to ascertain if the sets of data are similar and whether a null hypothesis is correct. If a null hypothesis is proven to be correct, the data will be viewed as reliable. T-testing is used by market research companies to assess if their data has come from the same population or if it occurred by chance.

T-testing is one type of hypothesis testing, however there are many others, such as: analysis of variance test, Z-test, chi-squared test and F-test. A T-test can be better than the other methods of hypothesis testing because it is more suited to smaller groups of data, which also makes it cost-efficient. The general formula of a T-test is also easy to follow so no previous statistical training is required. 

There are also multiple types of T-tests: one sample T-test (used to evaluate whether the mean average of a group is the same as the target population), two sample T-test (used to evaluate whether the mean average of a group is the same as another group) and paired sample T-test (used to evaluate whether the mean average of one group is the same at different times).

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