Enter a z, t, chi-square, or F statistic with the required degrees of freedom and choose the relevant tail.
Interpret the existing p-value without confusing statistical significance with effect size or practical importance.
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Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.
Use the p-value already calculated above, then compare it with your pre-specified alpha without hiding the distribution, tail or degrees of freedom.
Calculate a p-value above, then refresh this panel. Statistical significance is not effect size or practical importance.
Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.
The same numerical statistic can produce different p-values under different reference distributions or tail choices. Select z, t, chi-square or F only after the test and its assumptions are established, and choose one- or two-sided inference from the pre-specified hypothesis rather than from the observed result.
t, chi-square and F calculations require the appropriate degrees of freedom. A plausible statistic with the wrong df can return a precise-looking but incorrect probability. Preserve the df derivation with the result so the calculation can be reproduced independently.
Comparing p with α can support a pre-defined decision rule, but a small p-value does not mean the effect is large or practically important. Pair the p-value with the estimate, effect size and interval appropriate to the study.
The probability is computed under a null model and reference distribution. Dependence, selection bias, violated variance assumptions or an inappropriate test can invalidate the interpretation even if the arithmetic is correct. Use the visual threshold panel as evidence, not as a replacement for study-design review.
For a two-sided z test, statistics of equal magnitude and opposite sign should return the same p-value. A z value near zero should produce a large two-sided p-value, while a very large magnitude should produce a small one. These sanity checks help detect tail-selection mistakes.
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