t-Test Calculator (Biology)

A t-test compares the means of two groups to determine if they are statistically different. In biology, use it to compare treatment vs control groups, different species measurements, or pre/post treatment data when you have continuous numerical data.

Unpaired t-tests compare two independent groups (e.g., males vs females). Paired t-tests compare the same subjects before and after treatment (e.g., blood pressure before and after medication). Choose based on your experimental design.

The p-value indicates the probability of observing your results if there's no real difference between groups. If p < 0.05, your results are statistically significant, meaning the difference is likely real, not due to chance.

Cohen's d measures the magnitude of difference between groups. Small effect: d = 0.2, Medium: d = 0.5, Large: d = 0.8. A large effect size means the biological difference is substantial, even if statistically significant.

Your data should be approximately normally distributed, observations should be independent, and for unpaired t-tests, both groups should have similar variances. Small departures from normality are usually acceptable with sample sizes > 15.

Use Welch's t-test when your two groups have unequal variances (one group has much more spread than the other). It's more robust and doesn't assume equal variances like the standard unpaired t-test.

Generally, n ≥ 15 per group provides reasonable results. Smaller samples (n < 10) require very normal data. For detecting small biological effects, you may need 30+ subjects per group for adequate statistical power.

Report the t-statistic, degrees of freedom, p-value, and effect size. Example: 't(46) = 2.34, p = 0.023, d = 0.68'. Include means ± standard deviations for both groups and the 95% confidence interval for the difference.