How do I run a two-sample t-test in R and interpret the p-value?

To run a two-sample t-test in R, you use the t.test() function.

How to run the test

Assuming you have two vectors of data, group1 and group2, the code is:

t.test(group1, group2)

If your data is in a data frame (e.g., a column named values and a column named group), use the formula interface:

t.test(values ~ group, data = my_data)

How to interpret the p-value

The p-value tells you how likely it is to observe the difference between your groups if there were actually no difference at all (the "null hypothesis").

• If the p-value is small (typically < 0.05): You have evidence to reject the null hypothesis. This suggests that the difference between your two groups is statistically significant.
• If the p-value is large (typically ≥ 0.05): You do not have enough evidence to claim a significant difference. It does not prove the groups are exactly the same, only that any observed difference could reasonably be due to random chance.

Important considerations:

1. Assumptions: By default, R performs a Welch’s t-test, which does not assume the two groups have equal variances. This is generally the safest choice.
2. Direction: The default test is two-sided. If you specifically want to test if one group is strictly greater or smaller than the other, you can add alternative = "greater" or alternative = "less" to the function.