Statistics student
Understand why population and sample standard deviations differ.
Enter the same values, switch N versus n−1, and expand the displayed steps.
Can reproduce the divisor and distinguish σ/σ² from s/s².
Calculate population or sample standard deviation, variance, quartiles, IQR fences, z-scores, and descriptive distribution statistics from a strict number list.
A quick decision brief for this specific tool
Up to 10,000 strictly parsed finite numbers and an explicit population or sample selection
Mean, median, modes, variance, standard deviation, range, quartiles, IQR fences and flags, z-scores, histogram, and auditable calculation steps
Population variance divides squared deviations by N; sample variance uses n−1; quartiles use linear interpolation and potential outliers use the 1.5×IQR convention
Choose your path
Calculate and audit population or sample spread statistics from a strictly parsed dataset without mislabeling descriptive bands or outlier flags.
Understand why population and sample standard deviations differ.
Enter the same values, switch N versus n−1, and expand the displayed steps.
Can reproduce the divisor and distinguish σ/σ² from s/s².
Screen unusual observations without silently deleting them.
Review quartiles, 1.5×IQR fences, flags, histogram, and z-scores.
Treats a flag as an observation to investigate rather than proof of error.
Know when malformed or excessive input was rejected.
Paste comma/space/newline/semicolon data and inspect strict validation.
A token such as 12abc is reported instead of becoming 12, and no values beyond 10,000 are silently truncated.
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The tool first computes the arithmetic mean and sum of squared deviations. Population variance divides by N; sample variance divides by n−1. Standard deviation is the square root and therefore returns to the input unit.
σ = √(Σ(xᵢ−μ)²/N); s = √(Σ(xᵢ−x̄)²/(n−1))Multi-step calculations show intermediate values so you can audit each stage. This transparency helps catch data-entry errors before relying on the final number.
Quartiles use linear interpolation at positions (n−1)p. Potential outliers are values outside Q1−1.5×IQR and Q3+1.5×IQR. This is a descriptive screening convention and does not determine whether a value is valid.
Updated: July 2026
An analyst selects sample mode for measured parts, audits the n−1 divisor, and investigates IQR flags before deciding whether any observation reflects process variation or a data error.
A student compares population and sample results, expands the displayed squared-deviation steps, and confirms why the two standard deviations differ.
A researcher compares the mean, median, quartiles, histogram, and z-scores without assuming the distribution is normal or mislabeling mean ± SD as a confidence interval.
Paste up to 10,000 finite values, choose population (divide by N) or sample (divide by n−1), and audit the mean, variance, standard deviation, quartiles, histogram, z-scores, and 1.5×IQR flags. Invalid tokens are reported instead of silently discarded.