T-Test Calculator
Run a one-sample or two-sample (independent) t-test on your data and instantly get the t-statistic, degrees of freedom, p-value, and significance result.
Run a T-Test on Your Data
Paste in your data (comma or space separated), choose your test type and significance level, and instantly see whether the difference is statistically significant.
T-Test Estimator
Get a clear breakdown of your t-statistic and p-value.
How it Works
Understanding your t-test result
Enter Your Data
Paste in one or two sets of numeric values, depending on whether you’re comparing a sample to a fixed value or comparing two groups.
t-Statistic Calculated
We calculate the means, standard deviations, and standard error to work out how far apart your groups are, in standard error units.
p-Value Derived
Using the t-statistic and degrees of freedom, we calculate the two-tailed p-value — the probability of seeing this result by chance alone.
Significance Check
We compare the p-value against your chosen significance level to tell you whether the difference is statistically significant.
Interpreting Your T-Test Result
A quick guide to common significance levels and what they mean for your result.
| Significance Level (α) | Confidence Level | Typical Use Case |
|---|---|---|
| 0.10 | 90% | Exploratory research, early-stage A/B tests. |
| 0.05 | 95% | The most common threshold across most fields. |
| 0.01 | 99% | Medical research, high-stakes decisions. |
| 0.001 | 99.9% | Physics and other very high-precision fields. |
| Cohen’s d ≈ 0.2 / 0.5 / 0.8 | — | Conventionally small / medium / large effect sizes. |
T-Test FAQ
Answers to the most frequently asked questions about t-tests, p-values, and statistical significance.
A t-test is a statistical test used to determine whether there is a significant difference between the means of one or two groups. It is commonly used in research, A/B testing, and quality control to check whether an observed difference is likely to be real or simply due to random chance.
A one-sample t-test compares the mean of a single group against a known or hypothesized value. A two-sample (independent) t-test compares the means of two separate, unrelated groups to see if they differ significantly from each other.
The p-value tells you the probability of observing your result, or something more extreme, if there were truly no difference between the groups. A p-value below your chosen significance level, commonly 0.05, is usually taken as evidence of a statistically significant difference.
Welch’s t-test is a version of the two-sample t-test that does not assume the two groups have equal variances. It adjusts the degrees of freedom accordingly, making it a safer default than the standard pooled-variance t-test in most practical situations.