Grouped Data Standard Deviation Calculator

Enter class midpoints and frequencies to find the grouped standard deviation.

How it is calculated

Enter the class midpoints (m) and the frequency (f) of each class, separated by spaces or commas, with the same number of entries in both lists. With N = Σf, the tool computes the weighted mean x̄ = Σ(f·m) ÷ N and the population variance σ² = Σ f·(m − x̄)² ÷ N. The standard deviation is σ = √σ². It divides by N, not N − 1, so it treats the table as the whole population. Every value in a class is assumed to sit at its midpoint, so the result is an estimate. Frequencies are counts of observations, so they must be whole numbers and not negative; a fractional or negative frequency, lists of different lengths, or a total frequency of zero show no result. Midpoints may be negative or decimal.

Worked example

Classes 0–10, 10–20, 20–30, 30–40, 40–50 have midpoints 5, 15, 25, 35, 45 and frequencies 4, 10, 18, 12, 6, so N = 50. Σ f·m = 20 + 150 + 450 + 420 + 270 = 1310, and the mean is 1310 ÷ 50 = 26.20. The weighted squared deviations are 4×449.44 + 10×125.44 + 18×1.44 + 12×77.44 + 6×353.44 = 6128. Variance: 6128 ÷ 50 = 122.56. Standard deviation: √122.56 = 11.07.

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Common mistakes

  • Entering the class limits (0–10) or the class width instead of the midpoints. The midpoint of 0–10 is 5, and there must be one midpoint per class.
  • Giving lists of different lengths, such as 5 midpoints and 4 frequencies. Each midpoint needs exactly one frequency, or there is no result.
  • Entering relative frequencies such as 0.1, 0.2, 0.4 or percentages with decimals. Frequencies must be whole counts, so fractional values show no result; use the original counts from the table.
  • Expecting the sample standard deviation. This tool divides by N, the population version; for a sample the usual formula divides by N − 1, which gives a slightly larger value.
  • Typing a decimal comma. Commas separate numbers in these lists, so 12,5 is read as 12 and 5; write 12.5.

About this calculator

Grouped data arrives already sorted into class intervals with a count (frequency) for each one, rather than as a list of raw individual values — a typical example is a frequency table showing how many people fall into each age bracket. Since the exact individual values inside each class are unknown, the midpoint of each class interval stands in as the best estimate for every value in that class, and the population standard deviation and variance are then computed using those midpoints weighted by their frequencies.

This calculation is standard in introductory statistics courses precisely because so much real-world data — census age brackets, income ranges, exam score bands, histogram bins — comes pre-grouped rather than as raw individual measurements, so students and analysts need a method for estimating spread from summarized frequency-table data rather than from a full data set.

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