Independent t-tests, the easy way
As you saw in the last chapter, the t.test() function makes it easy to perform t-tests. This applies for independent t-tests in addition to dependent t-tests.
For an independent t-test, we specify these arguments to t.test():
x: Column of data containing the first group's intelligence scoresy: Column of data containing the second group's intelligence scoresvar.equal: Whether to assume the variance is equal in both groups. We do make this assumption here, so it should beTRUE
Note that we don't have to use the paired argument for an independent t-test, since this is the default behavior of the t.test() function. Check out ?t.test for more info.
To compute Cohen's d quickly, use cohensD() with three arguments:
x: Column of data containing the first group's intelligence scoresy: Column of data containing the second group's intelligence scoresmethod: Version of Cohen's d to compute, which should be"pooled"in this case
You should get the same values you got "by hand" in the previous exercises.
This exercise is part of the course
Intro to Statistics with R: Student's T-test
Exercise instructions
The wm_t08 and wm_t19 datasets have been loaded into your workspace.
- Apply
t.test()to test whether there's a significant difference between groups who trained for 19 days verses those who trained for only 8 days. Recall that intelligence score gains are contained in thegaincolumn of each data frame. Don't save the result. - Apply
cohensD()to compute Cohen's d. Use the same columns as inputs forxandy. Don't save the result.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
## The subsets wm_t08 and wm_t19 are preloaded in your workspace
# Conduct an independent t-test
# Calculate Cohen's d