A frequency distribution table organizes data to show how often each value occurs.
Relative frequency = frequency ÷ total count.
Cumulative frequency adds up all previous frequencies.
Reach for this tool when characterising data for research, quality control, or business analysis. The results follow standard statistical definitions and are compatible with further inferential analysis.
Choosing population standard deviation when the data is a sample is the most common statistical error with this type of calculation. Unless your dataset covers every possible member of the group, use sample SD (with n−1 denominator) for valid inference.
A researcher reports descriptive statistics for a survey dataset of 150 responses. Standard deviation is required alongside the mean for any subsequent t-test or ANOVA — the calculator produces both the sample and population variants so the correct one can be cited in the methods section.
It shows each distinct value in a dataset and how many times it occurs.
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