Tea comes in many varities, Orange Pekoe, Earl Grey, Dragonwell and likewise so does T-Tests:  Paired and Independent (assuming equal variance) and (Assuming unequal variances)

They are good for comparing the mean of two samples, or one sample to a known value

Paired or Dependent Samples T-Test

The Paired T-Test is used when you are measuring the same subjects on the same test on two different occasions.  For example, from the Savvy Survey from several years back, there was a pre-test and a post-test to determine if there was a change in

# paired t-test t.test(y1,y2,paired=TRUE) # where y1 & y2 are numeric

For a quick how to do a T test in R follow this hyperlink: Click Here

Independent Samples T-Tests

You use Independent sample when you are testing two groups on the same measure, but those groups are not paired, as in the example before. You can use it when you are using survey methods. How did social workers on the Kansas Side perceive their safety versus those on the Missouri side?

Also you can use it in experiments, where you give one group an intervention and the other group is the control.

Follow this link to see an Independent samples T-Test in Action: Get yer Indie T Tests

One-Samples T-Test AKA Single-Sample T-Test

We conduct a one sample test if we have a single sample that we want to compare to some known outcome. The score as reported in a journal for example.

Follow this link to see an Single sample T-Test in Action: Single T Tests