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P Value Calculator For Student T Test

Calculates p value for student t test using p = P(T ≥ |t|) from the Student t-distribution with df degrees of freedom, in real time in your browser.

Last updated: July 2026 · Free · No sign-up required

Results

Enter values above and click Calculate to see your result instantly.

Inside the P Value Calculator For Student T Test

Every result on this page comes from a real formula — p = P(T ≥ |t|) from the Student t-distribution with df degrees of freedom — computed live in your browser the moment you press the button.

If you are new to p value calculator for student t test, start with the worked example in the methodology section below — it walks the formula end-to-end with real numbers — then use the quick-reference table for common scenarios.

Quick reference

InputMeaning
t-statisticHow many standard errors from H₀
Degrees of freedomn − 1 (one sample)
TailsOne-tailed or two-tailed test
p-valueProb. of a result this extreme if H₀ true
α (alpha)Significance threshold, often 0.05

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Below the calculator we link a small set of plain-English explainer pages — "What is a percentage?", "Why does PEMDAS matter?", and so on. They cover the underlying concept in 4–6 short paragraphs. Read those before the calculator if the topic is new, or after if you want the extra context.

Interpretation guide

p-valueDecision at α = 0.05
p < 0.01Strong evidence against H₀
p < 0.05Statistically significant — reject H₀
0.05 ≤ p < 0.10Marginal; not significant at 5%
p ≥ 0.10Fail to reject H₀

Formula & methodology

Formula: p = P(T ≥ |t|) from the Student t-distribution with df degrees of freedom

How the result is calculated

A t-test compares a sample against a hypothesis using the t-statistic and the Student t-distribution. The p-value is the tail probability of seeing a t at least this extreme if the null hypothesis were true; a two-tailed p doubles the one-tailed tail area.

  1. Compute the t-statistic and degrees of freedom (df = n − 1 for one sample).
  2. Look up the tail area beyond |t| in the t-distribution for that df.
  3. Use that area for a one-tailed test, or double it for a two-tailed test; reject H₀ if p < α.

Example

With t = 2.10 and df = 19, the two-tailed p-value is about 0.049. Since 0.049 < 0.05, the result is statistically significant.

Frequently asked questions

What is a p-value in a t-test?
The p-value is the probability of obtaining a t-statistic at least as extreme as the one observed, assuming the null hypothesis is true. A small p-value suggests the data are unlikely under the null.
What is the difference between one-tailed and two-tailed?
A one-tailed test checks for a difference in a single direction and uses one tail of the distribution. A two-tailed test checks for any difference and uses both tails, so its p-value is double the one-tailed value.
What p-value is statistically significant?
By convention a p-value below 0.05 is considered statistically significant, meaning you reject the null hypothesis. Stricter studies use 0.01. The threshold (alpha) should be chosen before running the test.
How do I find degrees of freedom for a t-test?
For a one-sample or paired t-test, degrees of freedom equal the sample size minus one (n − 1). For a two-sample test the formula is more involved and depends on the variances and sizes.
Does a small p-value prove the effect is large?
No. A p-value only addresses whether an effect is likely non-zero, not how big it is. Always report an effect size and confidence interval alongside the p-value.
How do I get a p-value from a t score?
You need the t statistic and the degrees of freedom (n − 1 for one sample). Enter both and pick one- or two-tailed: t = 2.10 with df = 20 gives p ≈ 0.049 two-tailed. The same t with df = 5 gives p ≈ 0.09 — small samples demand bigger t values, which is exactly what the t-distribution's heavy tails encode.
When should I use a paired t-test instead of a two-sample test?
Use paired when the same subjects are measured twice — before/after training, left/right comparisons. The test works on the differences: for each pair compute after − before, then run a one-sample t-test on those differences against zero. Pairing removes between-subject variability, so it detects smaller effects than an unpaired test on identical data.
What does a t-score percentile mean?
It's the share of the t-distribution below your score at the given df — t = 1.72 with df = 20 sits at the 95th percentile, meaning only 5% of values land higher by chance. A one-tailed p-value is just 100 minus that percentile, expressed as a proportion.

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