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Standard Deviation Calculator

Quantify the dispersion and spread of dataset observations relative to their arithmetic mean. Computes both Bessel-corrected sample standard deviation (s) and population standard deviation (σ).

Standard Deviation (s / σ): 0

Enter values separated by commas, spaces, or tabs

Choose sample if evaluating a subset; population if whole universe

Standard Deviation (s / σ)
0.0000

How to Use This Calculator

  1. Enter or paste numbers separated by commas or spaces.
  2. Select Sample (n-1) or Population (N) mode.
  3. View the standard deviation, variance, and mean instantly.

Formula & Calculation Methodology

Sample s = √[ Σ(x − x̄)² ÷ (n − 1) ] | Population σ = √[ Σ(x − μ)² ÷ N ]

Standard deviation measures the average distance of observations from their mean. Dividing by n-1 for samples eliminates downward bias when estimating population parameters.

s / σStandard deviation (sample / population)
xIndividual data observation
x̄ / μArithmetic mean
n / NSample size / Population size

Worked Example

Evaluating sample: 10, 12, 23, 23, 16, 23, 21, 16

numbers: 10, 12, 23, 23, 16, 23, 21, 16
type: sample
Result: s = 5.2372 (Variance = 27.4286, Mean = 18.0000)

Evaluating population: 2, 4, 4, 4, 5, 5, 7, 9

numbers: 2, 4, 4, 4, 5, 5, 7, 9
type: population
Result: σ = 2.0000 (Variance = 4.0000, Mean = 5.0000)

Frequently Asked Questions

When should I choose Sample versus Population?↓

Choose Sample if your data represents a portion or survey of a wider group. Choose Population only when you possess all existing measurements in existence.

Why does Bessel correction divide by n - 1?↓

Dividing by n underestimates population variance because sample points are naturally closer to the sample mean than to the true population mean. Using n - 1 corrects this bias.

Can standard deviation ever be negative?↓

No. Because it is calculated by taking the principal square root of non-negative squared deviations, standard deviation is always ≥ 0.

What is a limitation of standard deviation?↓

Like the mean, standard deviation is sensitive to extreme outliers, as squaring deviations magnifies large discrepancies.

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Source Citation & Disclaimer

Informational tool only; not licensed tax, legal, or financial advice.