Enter your test statistic and select the distribution type (Z, t, F, r, or Chi-Square) to compute your p-value. The calculator returns both the one-tailed and two-tailed p-values, plus a significance interpretation based on the standard α = 0.05 threshold — helping you determine whether to reject the null hypothesis.
Two-Tailed P-Value
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One-Tailed P-Value
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Significance Result
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Significance Level (α)
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A p-value is the probability of observing a test result at least as extreme as the one measured, assuming the null hypothesis is true. It ranges from 0 to 1. A small p-value (typically ≤ 0.05) suggests strong evidence against the null hypothesis, while a large p-value suggests the data are consistent with it.
If the p-value is less than or equal to your chosen significance level (α), you reject the null hypothesis — the result is statistically significant. If the p-value is greater than α, you fail to reject the null hypothesis. For example, a p-value of 0.03 at α = 0.05 indicates a statistically significant result.
A two-tailed test checks for effects in both directions (greater than or less than), while a one-tailed test checks only one direction. The two-tailed p-value is simply twice the one-tailed p-value. Most scientific research uses two-tailed tests by default unless there is a strong directional hypothesis.
A Z-score measures how many standard deviations a data point is from the mean of a standard normal distribution. In hypothesis testing, you compute a Z-score from your sample and then convert it to a p-value to assess significance. Z-scores are used when the population standard deviation is known or the sample size is large.
A T-score is similar to a Z-score but is used when the sample size is small (typically n < 30) or when the population standard deviation is unknown. The t-distribution has heavier tails than the normal distribution, and its shape depends on the degrees of freedom. You need the degrees of freedom (n − 1 for a one-sample test) to compute the p-value.
The F-statistic is used in ANOVA and regression analysis to compare variances between groups or test the overall significance of a model. It requires two degrees of freedom values: one for the numerator (number of groups minus one) and one for the denominator (total observations minus number of groups). A large F-value generally corresponds to a small p-value.
The chi-square statistic is used to test relationships between categorical variables or to assess goodness of fit. It measures how much observed frequencies differ from expected frequencies. You need the degrees of freedom (typically number of categories minus one) to convert the chi-square value to a p-value.
No. P-values are probabilities and must always fall between 0 and 1. A p-value of 0 would mean it is impossible to observe your result by chance, and a p-value of 1 means you would always observe a result at least as extreme. If you get a value outside this range, there is likely a calculation error.