t-Test Calculator (Biology)

t-Test Calculator (Biology). A t-test is a statistical method used in biology to determine whether the means of two groups — such as a control and treatment group — are significantly different from each other. Select your type of t-Test (unpaired, Welch's, or paired) and significance level, then enter each group's sample mean, sample size, and standard deviation into the t-Test Calculator. Results include the t-statistic, p-value, degrees of freedom, mean difference, 95% confidence interval, Cohen's d effect size, and a plain-language significance verdict. Also try the calculate Sample Size Per Group, Total Sample Size & Effect Size (Cohen's d) — Sample Size (Biology).

Type of t-Test *

Significance Level (α) *

Group 1 Name *

Group 2 Name *

Sample Mean (x̄₁) *

Sample Size (n₁) *

Standard Deviation (s₁) *

Sample Mean (x̄₂) *

Sample Size (n₂) *

Standard Deviation (s₂) *

Results

t-Statistic

p-value

Degrees of Freedom

Mean Difference

95% CI Lower

95% CI Upper

Cohen's d (Effect Size)

Result

Results Table

What is a t-test and when should I use it in 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. See also our calculate Gini Coefficient Gini Coefficient (G).

What's the difference between unpaired and paired t-tests?

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.

What does the p-value tell me about my biological data?

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.

How do I interpret Cohen's d effect size?

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.

What assumptions must my biological data meet for t-tests?

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.

When should I use Welch's t-test instead of standard t-test?

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.

What sample size do I need for reliable t-test results?

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.

How do I report t-test results in my biology research?

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. You might also find our Correlation Coefficient Calculator (Biology) useful.