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T-Test Calculator

Test whether two groups are significantly different — compute p-value in seconds

The t-test answers a fundamental question: is the difference between two groups real, or could it be due to random chance? It produces a p-value: if p < 0.05, the difference is statistically significant at the 95% confidence level.

This tool loads a sample dataset of 30 test scores from two groups — Control (standard curriculum) and Treatment (new curriculum). Click Link Data to run a two-sample t-test, show the box plot comparison, and see whether the difference is significant.

Paste your own two-group CSV to test your own hypothesis.

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FAQ

What is a p-value?
The p-value is the probability of observing a difference as large as the one in your data (or larger) if the null hypothesis were true. A p-value < 0.05 means there is less than a 5% chance the difference occurred by random chance — conventionally called "statistically significant."
What is the null hypothesis in a t-test?
The null hypothesis (H₀) states that the two group means are equal: μ₁ = μ₂. The t-test asks: given the data, what is the probability of observing the measured difference if H₀ were true? Low p-value → reject H₀.
What is the difference between one-tailed and two-tailed tests?
A two-tailed test asks "is there any difference?" (either direction). A one-tailed test asks "is Group A higher than Group B?" and has more power to detect a difference in that specific direction. Use two-tailed unless you had a directional hypothesis before collecting data.
What sample size do I need for a t-test?
The t-test works even with small samples (n ≥ 5 per group), but small samples have low statistical power — they may miss real effects. A common target is n ≥ 30 per group. The AI can tell you whether your sample size is adequate.

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