Variance Calculator | Measure Data Spread and Variability

Variance and standard deviation both measure dispersion, but they serve different purposes. Variance (in squared units) is useful for statistical calculations and comparisons between datasets. Standard deviation (in the same units as the original data) is more intuitive for interpretation and is often preferred when reporting results. For example, if analyzing test scores measured in points, the standard deviation tells you the average deviation in points, while variance gives squared points.

Higher variance indicates greater dispersion or spread in your data, while lower variance suggests data points cluster closer to the mean. There's no universal threshold for "high" or "low" variance—interpretation depends on your specific context. Compare variance to the scale of your data and to variance in similar datasets. In financial analysis, high variance might indicate higher risk; in manufacturing, it could suggest inconsistent quality control.

No, variance cannot be negative. Since variance is calculated by squaring deviations from the mean, the result is always zero or positive. A variance of zero occurs only when all values in the dataset are identical, meaning there is no variability at all. The higher the variance, the more spread out the data points are from the mean.