### T Tests

There are basically three types of t tests.  We are going to look at each one in turn, that is, how to perform and interpret the output.  The three types are:

1. Independent Samples T test (Two-samples T test)
2. Paired Samples T Test (Dependent Samples T test)
3. One Sample T test

Assumptions underlying the use of t test

Before we look at the details of how to perform and interpret a t test, it is good idea for you to understand the assumptions underlying the use of t test.  The assumptions are:

• your data is normally distributed
• the variances between the groups are equal
• the sample size is adequate (at least 30 cases per group). This more important when you want to extrapolate the findings from your sample to the general population.

The p-value

In the interpretation of the t statistics, we will be looking at its p-value.  Generally, there are three situations where you will need to interpret the p-value:

1. If the p-value is greater than 0.05, the null hypothesis is accepted and the result is not significant.
2. If the p-value is less than 0.05 but greater than 0.01, the null hypothesis is rejected and the result is significant beyond the 5 percent level.
3. If the p-value is smaller than 0.01, the null hypothesis is rejected and the result is significant beyond the 1 percent level.

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