Standard Deviation Calculator
Paste a list of numbers to instantly compute both the sample standard deviation (s, dividing by n − 1) and the population standard deviation (σ, dividing by n), plus mean, median, IQR and sum of squares. A per-value deviation table is shown below so you can verify each step for homework or a stats report.
Sample std. dev. (s)
2.1381
Divides by n − 1 (Bessel's correction). Use when your data is a sample.
Sample variance (s²)
4.5714
Population std. dev. (σ)
2.0000
Divides by n. Use when your data is the entire population.
Population variance (σ²)
4.0000
Other descriptive statistics
- Count n
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- Sum Σx
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- Mean x̄
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- Median
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- Min
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- Max
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- Range
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- Q1 (25th pct.)
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- Q3 (75th pct.)
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- IQR
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- Sum of squares Σ(x−x̄)²
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- Sorted ascending
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Show working (per-value deviations & squares)
| i | xᵢ | xᵢ − x̄ | (xᵢ − x̄)² |
|---|
Formula
x̄ = Σxᵢ ÷ n SS = Σ(xᵢ − x̄)² // sum of squared deviations s² = SS ÷ (n − 1) // sample variance (Bessel's correction) s = √s² // sample standard deviation σ² = SS ÷ n // population variance σ = √σ² // population standard deviation
- · Which formula to use: if your numbers are the entire population (e.g. all 30 students in a class), use σ (divide by n). If they are a sample drawn from a larger population (polls, sampling), use s (divide by n − 1). HKDSE and IB typically default to sample standard deviation.
- · Bessel's correction (n − 1) compensates for the sample variance under-estimating the true population variance. The gap is largest when n is small: at n = 5, σ and s differ by ~12 %; at n = 100, just 0.5 %.
- · Q1 / Q3 use Type-7 linear interpolation — identical to Excel PERCENTILE.INC, Google Sheets PERCENTILE, and NumPy's "linear" default. Tukey or Type-6 implementations can give slightly different values for small datasets.
- · IQR (Q3 − Q1) is less sensitive to outliers and is the basis of the box plot. A common outlier rule flags values below Q1 − 1.5 × IQR or above Q3 + 1.5 × IQR.
- · Computations use standard IEEE 754 double-precision floating point. Extremely large datasets with very large values (≈ 10⁹+) may show minor rounding error.
- · Sources: Wikipedia "Standard deviation"; NIST/SEMATECH e-Handbook of Statistical Methods §1.3.5; Hyndman & Fan (1996) "Sample Quantiles in Statistical Packages" — Type-7 is the Excel / NumPy / R default.
Frequently asked
Should I use sample (n − 1) or population (n) standard deviation?
Rule of thumb: use population (σ) when your numbers ARE the entire group you care about; use sample (s) when they are a sample drawn from a larger population whose spread you want to estimate. Most textbook problems default to sample SD unless they explicitly say "population".
Why does sample standard deviation divide by n − 1 instead of n?
It's called Bessel's correction. When we use the sample mean we "spend" one degree of freedom, leaving n − 1 truly independent residuals. Dividing by n systematically underestimates the population variance; dividing by n − 1 makes s² an unbiased estimator of σ².
Is a larger standard deviation always "worse"?
No — standard deviation just measures spread. For exam scores, a small SD means everyone scored similarly. For stock returns, a larger SD means more volatility / risk. SD is always non-negative; if every input value is identical, σ = s = 0.
How many data points can I paste in?
The maths itself has no hard limit — browsers can comfortably handle tens of thousands of values. To keep the page responsive, the step-by-step working table only renders the first 100 rows, but every statistic above is computed over the full dataset.
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