Statistics Calculator
Paste data to get descriptive statistics, t-tests, ANOVA, correlation and regression, each with a plain-language reading
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Paste data from a spreadsheet to get descriptive statistics, normality tests, t-tests, ANOVA, chi-square tests and correlation and regression, each with a one-line reading. Use it to compare means or check a p-value without statistics software.
How to use
- Paste spreadsheet columns into "Data". A text first row becomes the column names, and each column is one group. "Load example" shows how it works.
- Choose the "Significance level α": 0.01, 0.05 or 0.10.
- Pick a tab ("Descriptive", "Normality", "t-test", "ANOVA", "Chi-square" or "Correlation & regression") and select the columns.
- Read the table and the reading below it, and take the results with "Copy table".
How it works
- The t-test covers one sample, two independent samples and paired data. Two independent samples use Welch's t-test by default, with the confidence interval of the difference and Cohen's d.
- Normality uses Shapiro–Wilk and Anderson–Darling. ANOVA also shows an equal-variance test and Welch's ANOVA; a 2×2 table also gets Fisher's exact test.
- Quartiles match Excel QUARTILE.INC. Outliers are values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR.
- This is for basic analysis and does not replace professional statistics software.
Questions and answers
- What does a p-value below 0.05 mean?
- At the 0.05 level, there is evidence of a difference or relationship. Whether it is large enough to matter is judged from the effect size and confidence interval.
- When should I use an independent or a paired t-test?
- Use two independent samples for separate groups, and the paired test for the same subjects measured twice. The paired test pairs values in the same row.
- How do I compare the means of three or more groups?
- Use one-way ANOVA. A small p-value means at least one mean differs, but not which one.
- What is the difference between r and R²?
- r gives the direction and strength of a straight-line relationship, from −1 to 1. R² is the share of the variation in Y explained by the line. In simple regression, R² = r².