Statistical Significance Calculator
Free online statistical significance calculator for biological and experimental data. Run a Welch, Student or paired t-test on two groups of values, or a chi-square test on a contingency table, and get the p-value, confidence interval, effect size and a plain-language interpretation, all computed in your browser.
What this statistical significance calculator does
Compare two groups of measurements with a t-test, or test whether two categorical variables are associated with a chi-square test of independence. Paste your values straight from a spreadsheet, choose the test and the significance level (α of 0.05, 0.01 or 0.10), and the calculator returns the test statistic, degrees of freedom, p-value, effect size and a plain-language verdict stating whether the result is statistically significant. All calculations run locally in your browser and no data is sent to a server.
t-test options: Welch, Student and paired
Welch's t-test is the default and does not assume equal variances between the two groups. Student's t-test assumes equal variances and pools them, and the paired t-test compares two measurements taken on the same subjects, such as before and after a treatment, and requires the same number of values in both groups. You can run two-sided tests or one-sided tests (A greater than B, or A less than B). Results include the mean difference with its confidence interval, Cohen's d as an effect size, and a summary table with n, mean, standard deviation, standard error, minimum and maximum for each group.
Chi-square test of independence for count data
Enter observed counts in a contingency table from 2×2 up to 5×5, for example genotype by phenotype, or treated versus control by responder versus non-responder. The calculator computes the chi-square statistic, degrees of freedom, p-value, Cramér's V as an effect size, and the table of expected counts. An optional Yates continuity correction applies to 2×2 tables, and a warning appears when expected counts fall below 5, a case where the chi-square approximation becomes unreliable and an exact test such as Fisher's is preferable.
Statistical Significance Calculator
Compare two groups with a t-test (Welch, Student or paired) or test association in a contingency table with chi-square. Paste your values, get the p-value, effect size and a plain-language reading — all computed in your browser.
All calculations run locally in your browser; no data is sent anywhere. The t-test assumes independent observations and approximately normally distributed values (or reasonably large samples); for strongly skewed data with small n, consider a non-parametric test such as Mann–Whitney. The chi-square test assumes independent observations and expected counts of at least 5 in most cells. A p-value below α indicates the observed result would be unlikely if there were no real effect — it does not measure the size or importance of an effect, and statistical significance is not proof of biological relevance. Decimal numbers use a dot (e.g. 12.5).
FAQ
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What does statistically significant mean?
A result is called statistically significant when its p-value is below the chosen significance level α, usually 0.05. It means the observed data would be unlikely if there were truly no effect or no difference. It does not mean the effect is large, important or biologically meaningful, which is why the calculator also reports an effect size. -
How do I interpret a p-value?
The p-value is the probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true. A small p-value is evidence against the null hypothesis. It is not the probability that the null hypothesis is true, and it says nothing about the size of the effect. -
Should I use Welch's, Student's or a paired t-test?
Use a paired t-test when the two sets of values are measurements on the same subjects, such as before and after treatment. For two independent groups, Welch's t-test is the safer default because it stays reliable when the group variances or sizes differ. Student's t-test is appropriate only when you are confident the variances are equal. -
When should I use a chi-square test instead of a t-test?
Use a t-test to compare the means of a continuous measurement, such as expression level or body weight, between groups. Use a chi-square test when your data are counts in categories, such as how many samples are mutant or wild type in each condition, and you want to know whether the groups differ in their distribution. -
What do Cohen's d and Cramér's V tell me?
They measure effect size, meaning how large a difference or association is, independently of sample size. As common rules of thumb, a Cohen's d of about 0.2 is small, 0.5 is medium and 0.8 or more is large. Cramér's V ranges from 0 to 1, where larger values indicate a stronger association. These thresholds are conventions and their meaning depends on your field. -
What assumptions and limits should I be aware of?
The t-test assumes independent observations and approximately normally distributed data, which matters most with small samples. The chi-square test assumes independent observations and expected counts of at least 5 in most cells. This calculator does not run non-parametric tests such as Mann-Whitney, or multi-group tests such as ANOVA. Is my data sent to a server? No, all calculations run in your browser.