To read a t-test, you primarily focus on the p-value provided in the test output.
Understanding the P-Value
The p-value is the key to interpreting your t-test results. It tells you the probability of observing your data, or more extreme data, if the null hypothesis were true. Here’s how to use it:
- Significance Level (Alpha): Before conducting a t-test, you must choose a significance level (alpha), often set at 0.05. This is the threshold for deciding if the result is statistically significant.
- Comparing P-Value and Alpha:
- If the p-value is less than or equal to your chosen alpha level (e.g., p-value ≤ 0.05): This means the results are considered statistically significant. You reject the null hypothesis, indicating that there is evidence to support a difference between the groups you are comparing.
- If the p-value is greater than your chosen alpha level (e.g., p-value > 0.05): This indicates that the result is not statistically significant. You fail to reject the null hypothesis, meaning there is insufficient evidence to claim a difference between the groups.
Finding the P-Value on Output
Statistical software like SPSS labels the p-value as “Sig.” You simply locate this value in the output, then follow the comparison process outlined above.
Steps to Read a T-Test:
- Locate the P-Value: Find the "Sig." value in your statistical output.
- Choose Alpha: Determine your chosen significance level (e.g., 0.05).
- Compare: Check if your p-value is less than or equal to your alpha level.
- Interpret Results: Decide if you reject or fail to reject the null hypothesis.
| Comparison | Conclusion |
|---|---|
| p-value ≤ alpha | Reject the null hypothesis |
| p-value > alpha | Fail to reject the null hypothesis |
Example
Let's say after running a t-test, your p-value was calculated as 0.02. If you chose an alpha of 0.05:
- 0.02 (p-value) is less than 0.05 (alpha).
- Therefore, you reject the null hypothesis. The difference observed is statistically significant.
