Statistics

One-Sample t-Test Calculator

Compute one-sample t-statistic from sample values and hypothesized mean.

Last validated: 2026-02-14

One-Sample t-Test Calculator tests whether a sample mean differs significantly from a reference value. It is useful for product metrics, process checks, and controlled experiments with unknown population variance. The tool computes t statistic and p-value from summary inputs. Use it to support evidence-based mean comparisons.

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Input Pattern

Enter values in the left panel, keep units explicit, run the calculation, then copy or share the result. Invalid fields are highlighted immediately.

One-Sample t-Test Inputs

Result

n: 7

Mean: 13.714286

Std dev: 1.799471

t-statistic: 1.050210

df: 6

How to use this tool

Enter sample mean, sample standard deviation, sample size, and hypothesized mean.
Run the t-test to compute statistic and p-value.
Interpret significance relative to your alpha threshold.

Worked Example

Auto-generated from the tool's current default or entered inputs.

Example Inputs

Values: 12, 15, 14, 13, 16, 11, 15
Mu0: 13.0
N: 7
Std dev: 1.7994708216848745
Stderr: 0.6801360408136047
T statistic: 1.0502100630210063
Df: 6

Expected Outputs

Mu0: 13
N: 7
Mean: 13.714286
Std dev: 1.799471

Interpretation

Mu0 is 13.0. Use this as a decision input, not as a standalone conclusion.
N is 7. Use this as a decision input, not as a standalone conclusion.
Mean is 13.714285714285714. Use this as a decision input, not as a standalone conclusion.
Interpret this value alongside sample quality and assumptions to avoid overconfidence.

Scenario Compare (A vs B)

Use this to compare two input sets and quantify change in key outputs.

Scenario A

Values

Mu0

Std dev

Stderr

T statistic

Scenario B

Values

Mu0

Std dev

Stderr

T statistic

Confidence and limitations

Outputs are deterministic formula results and depend entirely on input quality and units.
Results are for planning and learning; verify with domain standards before safety-critical use.
Interpretation depends on assumptions (distribution, independence, sample quality).

Formula References

Frequentist estimation and hypothesis-test formulas used in standard textbooks.
Interpretation depends on sample quality and assumptions.

Assumptions

Outputs are deterministic formula results and depend entirely on input quality and units.
Results are for planning and learning; verify with domain standards before safety-critical use.
Interpretation depends on assumptions (distribution, independence, sample quality).

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