Statistics
T-Test Explained: Compare Two Groups Statistically
Understand t-tests, when to use them, how to interpret results, and what p-values mean in statistical analysis.
A t-test determines if there's a statistically significant difference between the means of two groups. It's one of the most common statistical tests.
Types of T-Tests
- Independent samples: Compare two separate groups (men vs women)
- Paired samples: Compare same group at different times (before vs after)
- One-sample: Compare one group to a known value
T-Test Assumptions
- Data is normally distributed
- Observations are independent
- For independent t-test: equal variances (use Welch's t-test if not)
Understanding P-Values
The p-value tells you the probability of observing your results (or more extreme) if the null hypothesis is true. P < 0.05 typically means "statistically significant."
Interpreting Results
If p < 0.05: Reject the null hypothesis—the groups are significantly different. If p ≥ 0.05: Fail to reject—the difference could be due to chance.
Frequently Asked Questions
When should I use a t-test?
Use a t-test to compare means between two groups. For more than two groups, use ANOVA. For non-normal data, consider non-parametric tests.
What does p < 0.05 mean?
There's less than a 5% probability of observing your results if there's actually no difference. This is conventionally considered "statistically significant."
What is the difference between one-tailed and two-tailed tests?
Two-tailed tests check for any difference (higher or lower). One-tailed tests check for difference in one direction only. Use two-tailed unless you have strong prior reason.