With regards to

**t-test**, the function`t.test()`

in R may be used. This is a rather simple function which performs both one- and two-sample t-tests (it is thus likely that we will meet that function elsewhere in bioST@TS).Assuming that you have stored your sample data in the variable

`data`

*,*the command to use is`t.test(data, alternative="XXX", mu = Y) `

where:- “XXX” shall be replaced by “greater” OR “less” OR “two.sided” depending of your alternative hypothesis H
_{a}. The null hypothesis H_{0}states that the sample mean is NOT different from the population mean. Your alternative hypothesis H_{a}is one of the following:- the sample mean is greater than the population mean >> use “greater”
- the sample mean is less than the population mean >> use “less”
- the sample mean is either smaller or greater than the population mean >> use “two.sided”.

- Y shall be replaced by the value of the population mean.

R returns several lines of text. One of them provides a **p-value** while the next line states the **alternative hypothesis **which depends on the parameter alternative=”XXX” that you have entered in the `t.test()`

. This alternative hypothesis H_{a} is considered valid when the p-value is less than 0.05.

Read more about `t.test()`

and find more options by clicking here or there or by simply typing `?t.test`

in the R console.

?t.test