T test
T-test is a type of inferential statistics used to determine if there is a significant difference between the means of two groups. The T-test is based on the Student's t-distribution, which is a probability distribution that is used to estimate population parameters when the sample size is small and/or when the population variance is unknown.
History[edit | edit source]
The T-test was developed by William Sealy Gosset, a chemist working for the Guinness brewery in Dublin, Ireland. Gosset published the T-test in the journal Biometrika in 1908 under the pseudonym "Student".
Types of T-tests[edit | edit source]
There are three main types of T-tests: independent samples T-test, paired sample T-test, and one-sample T-test.
Independent samples T-test[edit | edit source]
The independent samples T-test is used when two separate sets of independent and identically distributed samples are obtained, one from each of the two populations being compared.
Paired sample T-test[edit | edit source]
The paired sample T-test is used when the samples are dependent; that is, when there is only one sample that has been tested twice (repeated measures) or when there are two samples that have been matched or paired.
One-sample T-test[edit | edit source]
The one-sample T-test is used when we want to know whether our sample comes from a particular population but we do not have a second sample to compare it with.
Assumptions[edit | edit source]
The T-test assumes that the data are normally distributed and that the variances of the two populations are equal (homogeneity of variance). If these assumptions are not met, a different test, such as the Mann-Whitney U test, may be more appropriate.
See also[edit | edit source]
References[edit | edit source]
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