AllFreeCalculator

Standard Deviation Calculator

Paste a list of numbers. Get population and sample standard deviation, variance, mean and count — with the formula shown.

Sample standard deviation (s)

√[ Σ(xᵢ − x̄)² ÷ (n − 1) ]

Population standard deviation (σ)

√[ Σ(xᵢ − μ)² ÷ N ]

Sample variance s²

Population variance σ²

Mean

Count

Step by step

    What this standard deviation calculator does

    The standard deviation calculator measures how spread out your numbers are around the mean. It returns both flavours — sample standard deviation (s, divided by n − 1) and population standard deviation (σ, divided by N) — along with the corresponding variances, the mean and the count, plus a step-by-step trace of the calculation. Statisticians use one or the other depending on whether the data represents a sample or the entire population, so showing both removes the most common point of confusion.

    The formulas

    Sample: s = √[ Σ(xᵢ − x̄)² ÷ (n − 1) ]

    Population: σ = √[ Σ(xᵢ − μ)² ÷ N ]

    The only difference is the divisor — n − 1 (Bessel's correction) for samples and N for populations. Variance is the value before taking the square root, in squared units.

    How to calculate by hand

    1. Calculate the mean: sum the values and divide by the count.
    2. Subtract the mean from each value to get the deviations.
    3. Square each deviation.
    4. Sum all the squared deviations.
    5. Divide by N for population, or n − 1 for sample, to get variance.
    6. Take the square root to get standard deviation.

    The calculator runs every step and the table above shows you the working.

    Sample vs population: which to pick

    If your data set is everything you care about — every customer, every student, every test result — use the population formula. If your data is a sample drawn from a larger group and you want to estimate the spread in that larger group, use the sample formula. The n − 1 divisor exists because using the sample mean (rather than the true unknown population mean) makes the squared-deviation sum slightly smaller than it should be; dividing by n − 1 instead of n compensates and gives an unbiased estimator.

    Reading the result with the empirical rule

    For roughly normal (bell-curve) data, about 68% of values lie within one standard deviation of the mean, 95% within two, and 99.7% within three. So if a test has a mean of 70 with standard deviation 10, you would expect most students between 60 and 80, with very few below 50 or above 90. This is a quick gut check on whether a result is unusual or routine.

    Common mistakes

    • Mixing variance and standard deviation — variance is in squared units (cm²), standard deviation in the original units (cm).
    • Using the wrong divisor. Engineers and economists often use n − 1 by default; physics labs computing the spread of all their measurements use N.
    • Forgetting that standard deviation is only meaningful for numerical data — it does not apply to categories.

    Use with the other tools

    For a simpler summary, the average calculator returns mean, median, mode, range and sum. For percentages on individual values, see the percentage calculator.

    Frequently asked questions

    What is standard deviation?

    Standard deviation is a measure of how spread out a set of numbers is from their mean. A small standard deviation means the values are clustered near the average; a large one means they are widely scattered.

    What is the difference between population and sample standard deviation?

    Use population (divide by N) when your data is the entire group you care about. Use sample (divide by N − 1) when your data is a sample drawn from a larger population — the N − 1 correction (Bessel's correction) makes the estimate unbiased.

    What is variance?

    Variance is the square of the standard deviation: the average of the squared distances from the mean. It is in squared units (e.g. cm²) which is why we usually take the square root and report standard deviation instead.

    How do I calculate it by hand?

    1) Find the mean. 2) Subtract the mean from each value and square the result. 3) Sum those squared deviations. 4) Divide by N (population) or N − 1 (sample) to get variance. 5) Take the square root to get standard deviation.

    Why use N − 1 for a sample?

    Because the mean of a sample is itself an estimate, the sample tends to underestimate the true population spread. Dividing by N − 1 instead of N corrects for that bias, giving an unbiased estimate of the population variance.

    What does standard deviation tell me about my data?

    For roughly normal data, about 68% of values lie within 1 standard deviation of the mean, 95% within 2, and 99.7% within 3 (the empirical rule). It is the most common measure of how reliable an "average" really is.

    Worked example

    The data set: 4, 8, 6, 5, 3, 7.

    • Mean = (4+8+6+5+3+7) ÷ 6 = 33 ÷ 6 = 5.5
    • Deviations from mean: −1.5, 2.5, 0.5, −0.5, −2.5, 1.5
    • Squared deviations: 2.25, 6.25, 0.25, 0.25, 6.25, 2.25 → sum = 17.5
    • Population variance σ² = 17.5 ÷ 6 = 2.9167σ ≈ 1.7078
    • Sample variance s² = 17.5 ÷ 5 = 3.5s ≈ 1.8708

    The sample SD is slightly larger because it divides by 5 instead of 6 — Bessel's correction in action.

    Related calculators