Paste your data to calculate sample or population standard deviation — with variance, coefficient of variation, z-scores, σ-zone breakdown, and a histogram with normal curve overlay.
Paste numeric values separated by commas, spaces, or new lines. Minimum 2 values required.
Choose sample or population standard deviation, paste your data, and press Calculate to see the full breakdown.
| # | Value | Deviation (x − x̄) | Z-score | Visual |
|---|
Use sample (n−1) when your data is a sample drawn from a larger process — which is almost always the case in quality and manufacturing. Use population (n) only when you have every single data point from a closed, finite group. The n−1 denominator corrects for bias in estimating the true population variance.
The coefficient of variation (CV = σ/μ × 100%) expresses standard deviation as a percentage of the mean. This lets you compare variability across processes with different scales. A CV below 10% is generally low variation; above 30% signals high relative variability.
For data that is approximately normally distributed: 68% of values fall within ±1σ of the mean, 95% within ±2σ, and 99.7% within ±3σ. If your data deviates significantly from these expectations, it may be non-normal — consider a Box Plot or normality test before applying Cp/Cpk analysis.