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Descriptive statistics calculator

Paste one or more columns of numbers and get n, mean, SD, median, IQR, min/max and skewness — plus a copy-ready APA summary.

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Your data

One column of numbers per variable — separated by spaces, commas or new lines. Use a dot as the decimal separator.

n = 0
Runs in your browser — data never leaves this page.

When to use this

Descriptive statistics are the first step of almost every quantitative analysis: before testing any hypothesis, you summarize each variable’s central tendency (mean, median), spread (standard deviation, IQR, range) and shape (skewness). Use them to describe your sample in the Participants or Results section, to spot data-entry errors (an age of 250, a negative reaction time), and to decide which inferential tests are appropriate — strongly skewed data, for example, often calls for medians and nonparametric tests rather than means and t-tests.

Key assumptions

  • No statistical assumptions — these are summaries, not tests.
  • The mean and SD are only meaningful for interval or ratio data; for ordinal data report the median and IQR.
  • Means and SDs are sensitive to outliers — check min/max and skewness before relying on them.

Common mistakes

  • Reporting a mean for heavily skewed data (e.g. income, reaction times) where the median describes the typical case far better.
  • Confusing the standard deviation (spread of the data) with the standard error (precision of the mean).
  • Forgetting to report n and how missing values were handled.

Frequently asked questions

Should I report the mean or the median?
Report the mean with the standard deviation when the distribution is roughly symmetric. When the data are skewed or contain outliers, the median with the interquartile range (IQR) describes the typical value better. Looking at both — and at the skewness statistic — is the quickest check.
Is the standard deviation here the sample or population SD?
The sample standard deviation, computed with n − 1 in the denominator (Bessel’s correction). That is what you almost always want when your data are a sample from a larger population, and it is what SPSS, R and jamovi report by default.
How is skewness interpreted?
Values near 0 indicate a roughly symmetric distribution. As a rule of thumb, skewness beyond ±1 indicates clear asymmetry, and ±0.5 to ±1 moderate asymmetry. Positive skew means a long right tail (a few unusually large values).

Related free tools

Sample size calculatorSample size for two groupsEffect size (Cohen's d)One-sample t-testIndependent t-testPaired t-testOne-way ANOVAz-table (standard normal)t-table (critical values)Chi-square table

Stop copying numbers between tools

Inside Kahubi, the AI agent runs this analysis directly on your uploaded dataset — then writes the results section in your own writing style, with the statistics reported correctly.

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