
A statistical measure of the spread of data points around the mean, indicating variability.Variance is a statistical measure that quantifies how much a set of numbers spread out from their average (mean). It is calculated as the average of the squared differences from the mean. A high variance indicates that the data points are widely scattered, while a low variance means they cluster closely around the mean. In physics, variance is used to describe noise, measurement errors, and the distribution of physical quantities like energy or velocity.
The concept of variance was formalized by Ronald Fisher in 1918, building on earlier work by Carl Friedrich Gauss on least squares and by Francis Galton on regression.
For a dataset with N values x_i and mean μ, the variance σ² is given by σ² = (1/N) Σ (x_i - μ)². The square root of variance is the standard deviation (σ), which has the same units as the original data and is often more intuitive. Variance is additive for independent random variables, making it a fundamental tool in error propagation and statistical mechanics.
Variance is used in experimental physics to quantify measurement uncertainty, in quantum mechanics to describe the spread of observable values, and in cosmology to analyze the distribution of galaxies.
In quantum mechanics, the Heisenberg uncertainty principle is often expressed in terms of variances: the product of the variances of position and momentum must be at least (ħ/2)².
| X | variable | first random variable — random variable |
| Y | variable | second random variable — random variable |
| Var(X) | variable | variance of X — variance |
| Var(Y) | variable | variance of Y — variance |
| Var(X+Y) | variable | variance of the sum X+Y — variance |
| r | variable | distance between two points — distance metre |
| ν | variable | smoothness parameter |
| l | variable | length scale parameter — length metre |
| σ | variable | variance amplitude — variance square metre |
| Γ | operator | Gamma function, generalizes factorial — gamma function |
| K_ν | operator | Modified Bessel function of the second kind of order ν — modified bessel function second kind |
| x_t | variable | value of the process at time t — time series value |
| x_{t-1} | variable | value of the process at previous time — time series value |
| \phi | variable | autoregressive coefficient — autoregressive coefficient |
| \epsilon_t | variable | white noise innovation at time t — noise term |
| \sigma^2 | variable | variance of the noise — variance |
| X_n | variable | random variable indexed by n — random variable |
| d_s | variable | spectral dimension — spectral dimension |
| Var | operator | statistical variance of a random variable — variance |
| ~ | operator | asymptotically proportional to (up to a constant factor) — asymptotic equivalence |
| f | operator | some function of the spectral dimension — function |






