Summary
A test for significant differences between the means of two groups.
In depth
Compares two means; unpaired for independent groups, paired for before-after measures. Assumes approximately normal data. Result: p-value; p below 0.05 is conventionally significant. Student's t-test was developed by W. S. Gosset at Guinness.
Check assumptions of normality and equal variance (or use Welch's test). Report t, df and p. Mann-Whitney is the non-parametric alternative.
Example: comparing mean heart rates before and after exercise with a paired t-test.
The Student's t-test determines whether the means of two groups differ significantly, taking into account the variability and sample sizes. It calculates t = difference between means / standard error of the difference, then finds a p-value from the t distribution and degrees of freedom.
Variants: unpaired (independent samples, such as treated vs control plants), paired (same subjects measured before and after) and one-sample. Assumptions are roughly normal data and, for the standard version, similar variances; the non-parametric Mann-Whitney test is used otherwise. A p-value below 0.05 means the difference is statistically significant.
Example: comparing heights of plants grown with and without a fertiliser.
Common mistake: using multiple t-tests for three or more groups; use ANOVA.
Frequently asked questions
What is t-Test?
A test for significant differences between the means of two groups.
Which area of biology covers t-Test, and how advanced is it?
t-Test is one of the 51 topics in Lab Methods & Biostatistics. It is rated Intermediate, so it suits learners who already know the core vocabulary. Related topics in this area include ANOVA, Statistical Power, Standard Deviation.
Can you give an example of t-Test?
Comparing plant heights with and without fertiliser.
What should I learn before t-Test?
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to t-Test. Intermediate topics assume the Beginner vocabulary.
What topics are related to t-Test?
The closest topics to t-Test are ANOVA, Statistical Power, Standard Deviation, Standard Error and Error Bars. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand t-Test?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for t-Test. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
How is t-Test different from ANOVA?
t-Test: A test for significant differences between the means of two groups. ANOVA: Compares means across three or more groups by partitioning variance. Read both side by side and note what each one explains.
What is a common mistake about t-Test?
Using multiple t-tests for three or more groups; use ANOVA.
How should I study t-Test step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with ANOVA and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.