quantity

standard deviation

A measure of the amount of variation or dispersion of a set of values from its mean.
10 related articles · 4 formulas

Description

Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values. It tells you how spread out the numbers are around the mean (average). A small standard deviation means the values are clustered closely, while a large one indicates wide spread. In physics, it is used to characterize the uncertainty in measurements, the spread of particle velocities in a gas, or the noise in experimental data.

History

The concept was introduced by Karl Pearson in 1894, building on earlier work by Gauss and others on the normal distribution and least squares methods.

How it works

To compute it, you take the square root of the variance, which is the average of the squared differences from the mean. This gives a value in the same units as the original data, making it intuitive. In a normal distribution, about 68% of values lie within one standard deviation of the mean, 95% within two, and 99.7% within three—a rule of thumb used widely in quality control and error analysis.

In practice

Standard deviation is used in experimental physics to report measurement uncertainties, in particle physics to identify significant deviations from background noise, and in engineering to set tolerance limits.

Fun fact

The standard deviation of the cosmic microwave background temperature fluctuations is about 18 microkelvins, a tiny number that encodes the seeds of all structure in the universe.

Concept graph →

Formulas

$$\Delta\theta \geq \frac{1}{\sqrt{\mathcal{F}(\rho_\theta)}}$$
Estimation error is bounded by inverse square root of Fisher information.
What is in the formula
\Delta\thetavariableStandard deviation of the estimator for parameter theta — standard deviation
\mathcal{F}(\rho_\thvariableQuantum Fisher information of the state rho_theta — quantum fisher information
$$\mathcal{L} = \prod_i \exp\left( -\frac{1}{2} \left[ \frac{f(x_i) - y_i}{\sigma_i} \right]^2 \right)$$
Probability of observed data given a model with Gaussian errors.
What is in the formula
f(x_i)variablemodel prediction at input x_i — function value
y_ivariableobserved data point — data value
σ_ivariablestandard deviation of measurement error — standard deviation
∏operatorProduct over all data points i — product operator
expoperatorExponential function — exponential function
$$\log P(D|\theta) = -\frac{1}{2}\sum \left(\frac{(y_{\text{model}} - y_{\text{data}})^2}{\sigma^2} + \log(2\pi\sigma^2)\right)$$
Probability of data given model parameters with Gaussian errors.
What is in the formula
P(D|θ)variableprobability of data given parameters — probability
y_modelvariablemodel prediction — value
y_datavariableobserved data point — value
σvariablestandard deviation of Gaussian noise — standard deviation
logoperatornatural logarithm — logarithm
∑operatorsum over all data points — summation
$$c(v) = \sum_{\lambda} \frac{ (S_1(\lambda,v) - \bar{S}_1(v)) (S_2(\lambda) - \bar{S}_2) }{ \sigma^2(\lambda) }$$
Measures similarity between two spectra at Doppler shift v.
What is in the formula
cvariablecross-correlation value — cross correlation
vvariableDoppler shift velocity — velocity metre per second
λvariablewavelength — wavelength metre
S1variablefirst spectrum — spectrum
S2variablesecond spectrum — spectrum
S̄1variablemean of first spectrum over wavelength — mean spectrum
S̄2variablemean of second spectrum over wavelength — mean spectrum
σvariablestandard deviation of spectrum — standard deviation
∑operatorsum over wavelength bins — summation

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