In statistics, the **standard deviation** is a measure of the amount of variation or dispersion of a set of values.^{ [1] } A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.

- Basic examples
- Sample standard deviation of metabolic rate of northern fulmars
- Population standard deviation of grades of eight students
- Standard deviation of average height for adult men
- Definition of population values
- Discrete random variable
- Continuous random variable
- Estimation
- Uncorrected sample standard deviation
- Corrected sample standard deviation
- Unbiased sample standard deviation
- Confidence interval of a sampled standard deviation
- Bounds on standard deviation
- Identities and mathematical properties
- Interpretation and application
- Application examples
- Geometric interpretation
- Chebyshev's inequality
- Rules for normally distributed data
- Relationship between standard deviation and mean
- Standard deviation of the mean
- Rapid calculation methods
- Weighted calculation
- History
- Higher Dimensions
- See also
- References
- External links

Standard deviation may be abbreviated **SD**, and is most commonly represented in mathematical texts and equations by the lower case Greek letter sigma ** σ **, for the population standard deviation, or the Latin letter s, for the sample standard deviation.^{ [2] }

(For other uses of the symbol σ in science and mathematics, see Sigma § Science and mathematics.)

The standard deviation of a random variable, statistical population, data set, or probability distribution is the square root of its variance. It is algebraically simpler, though in practice less robust, than the average absolute deviation.^{ [3] }^{ [4] } A useful property of the standard deviation is that unlike the variance, it is expressed in the same unit as the data.

In addition to expressing the variability of a population, the standard deviation is commonly used to measure confidence in statistical conclusions. For example, the margin of error in polling data is determined by calculating the expected standard deviation in the results if the same poll were to be conducted multiple times. This derivation of a standard deviation is often called the "standard error of the estimate", or "standard error of the mean" when referring to a mean. It is computed as the standard deviation of all the means that would be computed from that population, if an infinite number of samples were drawn and a mean for each sample were computed.

The standard deviation of a population and the standard error of a statistic derived from that population (e.g., mean) are quite different but related (namely, by the inverse of the square root of the number of observations). The reported margin of error of a poll is computed from the standard error of the mean (or alternatively, from the product of the standard deviation of the population and the inverse of the square root of the sample size), and is typically about twice the standard deviation—the half-width of a 95 percent confidence interval.

In science, many researchers report the standard deviation of experimental data, and by convention, only effects more than two standard deviations away from a null expectation are considered statistically significant, by which normal random error or variation in the measurements is in this way distinguished from likely genuine effects or associations. The standard deviation is also important in finance, where the standard deviation on the rate of return on an investment is a measure of the volatility of the investment.

When only a sample of data from a population is available, the term *standard deviation of the sample* or *sample standard deviation* can refer to either the above-mentioned quantity as applied to those data, or to a modified quantity that is an unbiased estimate of the *population standard deviation* (the standard deviation of the entire population).

Logan^{ [5] } gives the following example. Furness and Bryant^{ [6] } measured the resting metabolic rate for 8 male and 6 female breeding northern fulmars. The table shows the Furness data set.

Sex | Metabolic rate | Sex | Metabolic rate |
---|---|---|---|

Male | 525.8 | Female | 727.7 |

605.7 | 1086.5 | ||

843.3 | 1091.0 | ||

1195.5 | 1361.3 | ||

1945.6 | 1490.5 | ||

2135.6 | 1956.1 | ||

2308.7 | |||

2950.0 |

The graph shows the metabolic rate for males and females. By visual inspection, it appears that the variability of the metabolic rate is greater for males than for females.

The sample standard deviation of the metabolic rate for the female fulmars is calculated as follows. The formula for the sample standard deviation is

^{ [2] }^{ [7] }

where are the observed values of the sample items, is the mean value of these observations, and *N* is the number of observations in the sample.

In the sample standard deviation formula, for this example, the numerator is the sum of the squared deviation of each individual animal's metabolic rate from the mean metabolic rate. The table below shows the calculation of this sum of squared deviations for the female fulmars. For females, the sum of squared deviations is 886047.09, as shown in the table.

Animal | Sex | Metabolic rate | Mean | Difference from mean | Squared difference from mean |
---|---|---|---|---|---|

1 | Female | 727.7 | 1285.5 | −557.8 | 311140.84 |

2 | Female | 1086.5 | 1285.5 | −199.0 | 39601.00 |

3 | Female | 1091.0 | 1285.5 | −194.5 | 37830.25 |

4 | Female | 1361.3 | 1285.5 | 75.8 | 5745.64 |

5 | Female | 1490.5 | 1285.5 | 205.0 | 42025.00 |

6 | Female | 1956.1 | 1285.5 | 670.6 | 449704.36 |

Mean of metabolic rates | 1285.5 | Sum of squared differences | 886047.09 |

The denominator in the sample standard deviation formula is *N* − 1, where *N* is the number of animals. In this example, there are *N* = 6 females, so the denominator is 6 − 1 = 5. The sample standard deviation for the female fulmars is therefore

For the male fulmars, a similar calculation gives a sample standard deviation of 894.37, approximately twice as large as the standard deviation for the females. The graph shows the metabolic rate data, the means (red dots), and the standard deviations (red lines) for females and males.

Use of the sample standard deviation implies that these 14 fulmars are a sample from a larger population of fulmars. If these 14 fulmars comprised the entire population (perhaps the last 14 surviving fulmars), then instead of the sample standard deviation, the calculation would use the population standard deviation. In the population standard deviation formula, the denominator is *N* instead of *N* − 1. It is rare that measurements can be taken for an entire population, so, by default, statistical computer programs calculate the sample standard deviation. Similarly, journal articles report the sample standard deviation unless otherwise specified.

Suppose that the entire population of interest was eight students in a particular class. For a finite set of numbers, the population standard deviation is found by taking the square root of the average of the squared deviations of the values subtracted from their average value. The marks of a class of eight students (that is, a statistical population) are the following eight values:

These eight data points have the mean (average) of 5:

First, calculate the deviations of each data point from the mean, and square the result of each:

The variance is the mean of these values:

and the *population* standard deviation is equal to the square root of the variance:

This formula is valid only if the eight values with which we began form the complete population. If the values instead were a random sample drawn from some large parent population (for example, they were 8 students randomly and independently chosen from a class of 2 million), then one often divides by 7 (which is *n* − 1) instead of 8 (which is *n*) in the denominator of the last formula. In that case, the result of the original formula would be called the *sample* standard deviation. Dividing by *n* − 1 rather than by *n* gives an unbiased estimate of the variance of the larger parent population. This is known as * Bessel's correction *.^{ [8] }^{ [9] }

If the population of interest is approximately normally distributed, the standard deviation provides information on the proportion of observations above or below certain values. For example, the average height for adult men in the United States is about 70 inches (177.8 cm), with a standard deviation of around 3 inches (7.62 cm). This means that most men (about 68%, assuming a normal distribution) have a height within 3 inches (7.62 cm) of the mean (67–73 inches (170.18–185.42 cm)) –one standard deviation –and almost all men (about 95%) have a height within 6 inches (15.24 cm) of the mean (64–76 inches (162.56–193.04 cm)) –two standard deviations. If the standard deviation were zero, then all men would be exactly 70 inches (177.8 cm) tall. If the standard deviation were 20 inches (50.8 cm), then men would have much more variable heights, with a typical range of about 50–90 inches (127–228.6 cm). Three standard deviations account for 99.7% of the sample population being studied, assuming the distribution is normal or bell-shaped (see the 68-95-99.7 rule, or the *empirical rule,* for more information).

Let *X* be a random variable with mean value *μ*:

Here the operator E denotes the average or expected value of *X*. Then the standard deviation of *X* is the quantity

(derived using the properties of expected value).

In other words, the standard deviation *σ* (sigma) is the square root of the variance of *X*; i.e., it is the square root of the average value of (*X* − *μ*)^{2}.

The standard deviation of a (univariate) probability distribution is the same as that of a random variable having that distribution. Not all random variables have a standard deviation, since these expected values need not exist. For example, the standard deviation of a random variable that follows a Cauchy distribution is undefined, because its expected value *μ* is undefined.

In the case where *X* takes random values from a finite data set *x*_{1}, *x*_{2}, ..., *x _{N}*, with each value having the same probability, the standard deviation is

or, using summation notation,

If, instead of having equal probabilities, the values have different probabilities, let *x*_{1} have probability *p*_{1}, *x*_{2} have probability *p*_{2}, ..., *x*_{N} have probability *p*_{N}. In this case, the standard deviation will be

The standard deviation of a continuous real-valued random variable *X* with probability density function *p*(*x*) is

and where the integrals are definite integrals taken for *x* ranging over the set of possible values of the random variable *X*.

In the case of a parametric family of distributions, the standard deviation can be expressed in terms of the parameters. For example, in the case of the log-normal distribution with parameters *μ* and *σ*^{2}, the standard deviation is

One can find the standard deviation of an entire population in cases (such as standardized testing) where every member of a population is sampled. In cases where that cannot be done, the standard deviation *σ* is estimated by examining a random sample taken from the population and computing a statistic of the sample, which is used as an estimate of the population standard deviation. Such a statistic is called an estimator, and the estimator (or the value of the estimator, namely the estimate) is called a sample standard deviation, and is denoted by *s* (possibly with modifiers).

Unlike in the case of estimating the population mean, for which the sample mean is a simple estimator with many desirable properties (unbiased, efficient, maximum likelihood), there is no single estimator for the standard deviation with all these properties, and unbiased estimation of standard deviation is a very technically involved problem. Most often, the standard deviation is estimated using the * corrected sample standard deviation * (using *N* − 1), defined below, and this is often referred to as the "sample standard deviation", without qualifiers. However, other estimators are better in other respects: the uncorrected estimator (using *N*) yields lower mean squared error, while using *N* − 1.5 (for the normal distribution) almost completely eliminates bias.

The formula for the *population* standard deviation (of a finite population) can be applied to the sample, using the size of the sample as the size of the population (though the actual population size from which the sample is drawn may be much larger). This estimator, denoted by *s*_{N}, is known as the *uncorrected sample standard deviation*, or sometimes the *standard deviation of the sample* (considered as the entire population), and is defined as follows:^{ [7] }

where are the observed values of the sample items, and is the mean value of these observations, while the denominator *N* stands for the size of the sample: this is the square root of the sample variance, which is the average of the squared deviations about the sample mean.

This is a consistent estimator (it converges in probability to the population value as the number of samples goes to infinity), and is the maximum-likelihood estimate when the population is normally distributed.^{[ citation needed ]} However, this is a biased estimator, as the estimates are generally too low. The bias decreases as sample size grows, dropping off as 1/*N*, and thus is most significant for small or moderate sample sizes; for the bias is below 1%. Thus for very large sample sizes, the uncorrected sample standard deviation is generally acceptable. This estimator also has a uniformly smaller mean squared error than the corrected sample standard deviation.

If the *biased sample variance * (the second central moment of the sample, which is a downward-biased estimate of the population variance) is used to compute an estimate of the population's standard deviation, the result is

Here taking the square root introduces further downward bias, by Jensen's inequality, due to the square root's being a concave function. The bias in the variance is easily corrected, but the bias from the square root is more difficult to correct, and depends on the distribution in question.

An unbiased estimator for the *variance* is given by applying Bessel's correction, using *N* − 1 instead of *N* to yield the *unbiased sample variance,* denoted *s*^{2}:

This estimator is unbiased if the variance exists and the sample values are drawn independently with replacement. *N* − 1 corresponds to the number of degrees of freedom in the vector of deviations from the mean,

Taking square roots reintroduces bias (because the square root is a nonlinear function, which does not commute with the expectation), yielding the *corrected sample standard deviation,* denoted by *s:*^{ [2] }

As explained above, while *s*^{2} is an unbiased estimator for the population variance, *s* is still a biased estimator for the population standard deviation, though markedly less biased than the uncorrected sample standard deviation. This estimator is commonly used and generally known simply as the "sample standard deviation". The bias may still be large for small samples (*N* less than 10). As sample size increases, the amount of bias decreases. We obtain more information and the difference between and becomes smaller.

For unbiased estimation of standard deviation, there is no formula that works across all distributions, unlike for mean and variance. Instead, *s* is used as a basis, and is scaled by a correction factor to produce an unbiased estimate. For the normal distribution, an unbiased estimator is given by *s*/*c*_{4}, where the correction factor (which depends on *N*) is given in terms of the Gamma function, and equals:

This arises because the sampling distribution of the sample standard deviation follows a (scaled) chi distribution, and the correction factor is the mean of the chi distribution.

An approximation can be given by replacing *N* − 1 with *N* − 1.5, yielding:

The error in this approximation decays quadratically (as 1/*N*^{2}), and it is suited for all but the smallest samples or highest precision: for *N* = 3 the bias is equal to 1.3%, and for *N* = 9 the bias is already less than 0.1%.

A more accurate approximation is to replace above with .^{ [10] }

For other distributions, the correct formula depends on the distribution, but a rule of thumb is to use the further refinement of the approximation:

where *γ*_{2} denotes the population excess kurtosis. The excess kurtosis may be either known beforehand for certain distributions, or estimated from the data.^{[ citation needed ]}

The standard deviation we obtain by sampling a distribution is itself not absolutely accurate, both for mathematical reasons (explained here by the confidence interval) and for practical reasons of measurement (measurement error). The mathematical effect can be described by the confidence interval or CI.

To show how a larger sample will make the confidence interval narrower, consider the following examples: A small population of *N* = 2 has only 1 degree of freedom for estimating the standard deviation. The result is that a 95% CI of the SD runs from 0.45 × SD to 31.9 × SD; the factors here are as follows:

where is the *p*-th quantile of the chi-square distribution with *k* degrees of freedom, and is the confidence level. This is equivalent to the following:

With *k* = 1, and . The reciprocals of the square roots of these two numbers give us the factors 0.45 and 31.9 given above.

A larger population of *N* = 10 has 9 degrees of freedom for estimating the standard deviation. The same computations as above give us in this case a 95% CI running from 0.69 × SD to 1.83 × SD. So even with a sample population of 10, the actual SD can still be almost a factor 2 higher than the sampled SD. For a sample population N=100, this is down to 0.88 × SD to 1.16 × SD. To be more certain that the sampled SD is close to the actual SD we need to sample a large number of points.

These same formulae can be used to obtain confidence intervals on the variance of residuals from a least squares fit under standard normal theory, where *k* is now the number of degrees of freedom for error.

For a set of *N* > 4 data spanning a range of values *R*, an upper bound on the standard deviation *s* is given by *s = 0.6R*.^{ [11] } An estimate of the standard deviation for *N* > 100 data taken to be approximately normal follows from the heuristic that 95% of the area under the normal curve lies roughly two standard deviations to either side of the mean, so that, with 95% probability the total range of values *R* represents four standard deviations so that *s ≈ R/4*. This so-called range rule is useful in sample size estimation, as the range of possible values is easier to estimate than the standard deviation. Other divisors *K(N)* of the range such that *s ≈ R/K(N)* are available for other values of *N* and for non-normal distributions.^{ [12] }

The standard deviation is invariant under changes in location, and scales directly with the scale of the random variable. Thus, for a constant *c* and random variables *X* and *Y*:

The standard deviation of the sum of two random variables can be related to their individual standard deviations and the covariance between them:

where and stand for variance and covariance, respectively.

The calculation of the sum of squared deviations can be related to moments calculated directly from the data. In the following formula, the letter E is interpreted to mean expected value, i.e., mean.

The sample standard deviation can be computed as:

For a finite population with equal probabilities at all points, we have

which means that the standard deviation is equal to the square root of the difference between the average of the squares of the values and the square of the average value.

See computational formula for the variance for proof, and for an analogous result for the sample standard deviation.

A large standard deviation indicates that the data points can spread far from the mean and a small standard deviation indicates that they are clustered closely around the mean.

For example, each of the three populations {0, 0, 14, 14}, {0, 6, 8, 14} and {6, 6, 8, 8} has a mean of 7. Their standard deviations are 7, 5, and 1, respectively. The third population has a much smaller standard deviation than the other two because its values are all close to 7. These standard deviations have the same units as the data points themselves. If, for instance, the data set {0, 6, 8, 14} represents the ages of a population of four siblings in years, the standard deviation is 5 years. As another example, the population {1000, 1006, 1008, 1014} may represent the distances traveled by four athletes, measured in meters. It has a mean of 1007 meters, and a standard deviation of 5 meters.

Standard deviation may serve as a measure of uncertainty. In physical science, for example, the reported standard deviation of a group of repeated measurements gives the precision of those measurements. When deciding whether measurements agree with a theoretical prediction, the standard deviation of those measurements is of crucial importance: if the mean of the measurements is too far away from the prediction (with the distance measured in standard deviations), then the theory being tested probably needs to be revised. This makes sense since they fall outside the range of values that could reasonably be expected to occur, if the prediction were correct and the standard deviation appropriately quantified. See prediction interval.

While the standard deviation does measure how far typical values tend to be from the mean, other measures are available. An example is the mean absolute deviation, which might be considered a more direct measure of average distance, compared to the root mean square distance inherent in the standard deviation.

The practical value of understanding the standard deviation of a set of values is in appreciating how much variation there is from the average (mean).

Standard deviation is often used to compare real-world data against a model to test the model. For example, in industrial applications the weight of products coming off a production line may need to comply with a legally required value. By weighing some fraction of the products an average weight can be found, which will always be slightly different from the long-term average. By using standard deviations, a minimum and maximum value can be calculated that the averaged weight will be within some very high percentage of the time (99.9% or more). If it falls outside the range then the production process may need to be corrected. Statistical tests such as these are particularly important when the testing is relatively expensive. For example, if the product needs to be opened and drained and weighed, or if the product was otherwise used up by the test.

In experimental science, a theoretical model of reality is used. Particle physics conventionally uses a standard of "5 sigma" for the declaration of a discovery.^{ [13] } A five-sigma level translates to one chance in 3.5 million that a random fluctuation would yield the result. This level of certainty was required in order to assert that a particle consistent with the Higgs boson had been discovered in two independent experiments at CERN,^{ [14] } and this was also the significance level leading to the declaration of the first observation of gravitational waves.^{ [15] }

As a simple example, consider the average daily maximum temperatures for two cities, one inland and one on the coast. It is helpful to understand that the range of daily maximum temperatures for cities near the coast is smaller than for cities inland. Thus, while these two cities may each have the same average maximum temperature, the standard deviation of the daily maximum temperature for the coastal city will be less than that of the inland city as, on any particular day, the actual maximum temperature is more likely to be farther from the average maximum temperature for the inland city than for the coastal one.

In finance, standard deviation is often used as a measure of the risk associated with price-fluctuations of a given asset (stocks, bonds, property, etc.), or the risk of a portfolio of assets^{ [16] } (actively managed mutual funds, index mutual funds, or ETFs). Risk is an important factor in determining how to efficiently manage a portfolio of investments because it determines the variation in returns on the asset and/or portfolio and gives investors a mathematical basis for investment decisions (known as mean-variance optimization). The fundamental concept of risk is that as it increases, the expected return on an investment should increase as well, an increase known as the risk premium. In other words, investors should expect a higher return on an investment when that investment carries a higher level of risk or uncertainty. When evaluating investments, investors should estimate both the expected return and the uncertainty of future returns. Standard deviation provides a quantified estimate of the uncertainty of future returns.

For example, assume an investor had to choose between two stocks. Stock A over the past 20 years had an average return of 10 percent, with a standard deviation of 20 percentage points (pp) and Stock B, over the same period, had average returns of 12 percent but a higher standard deviation of 30 pp. On the basis of risk and return, an investor may decide that Stock A is the safer choice, because Stock B's additional two percentage points of return is not worth the additional 10 pp standard deviation (greater risk or uncertainty of the expected return). Stock B is likely to fall short of the initial investment (but also to exceed the initial investment) more often than Stock A under the same circumstances, and is estimated to return only two percent more on average. In this example, Stock A is expected to earn about 10 percent, plus or minus 20 pp (a range of 30 percent to −10 percent), about two-thirds of the future year returns. When considering more extreme possible returns or outcomes in future, an investor should expect results of as much as 10 percent plus or minus 60 pp, or a range from 70 percent to −50 percent, which includes outcomes for three standard deviations from the average return (about 99.7 percent of probable returns).

Calculating the average (or arithmetic mean) of the return of a security over a given period will generate the expected return of the asset. For each period, subtracting the expected return from the actual return results in the difference from the mean. Squaring the difference in each period and taking the average gives the overall variance of the return of the asset. The larger the variance, the greater risk the security carries. Finding the square root of this variance will give the standard deviation of the investment tool in question.

Population standard deviation is used to set the width of Bollinger Bands, a widely adopted technical analysis tool. For example, the upper Bollinger Band is given as The most commonly used value for *n* is 2; there is about a five percent chance of going outside, assuming a normal distribution of returns.

Financial time series are known to be non-stationary series, whereas the statistical calculations above, such as standard deviation, apply only to stationary series. To apply the above statistical tools to non-stationary series, the series first must be transformed to a stationary series, enabling use of statistical tools that now have a valid basis from which to work.

To gain some geometric insights and clarification, we will start with a population of three values, *x*_{1}, *x*_{2}, *x*_{3}. This defines a point *P* = (*x*_{1}, *x*_{2}, *x*_{3}) in **R**^{3}. Consider the line *L* = {(*r*, *r*, *r*) : *r* ∈ **R**}. This is the "main diagonal" going through the origin. If our three given values were all equal, then the standard deviation would be zero and *P* would lie on *L*. So it is not unreasonable to assume that the standard deviation is related to the *distance* of *P* to *L*. That is indeed the case. To move orthogonally from *L* to the point *P*, one begins at the point:

whose coordinates are the mean of the values we started out with.

Derivation of |
---|

is on therefore for some . The line is to be orthogonal to the vector from to . Therefore: |

A little algebra shows that the distance between *P* and *M* (which is the same as the orthogonal distance between *P* and the line *L*) is equal to the standard deviation of the vector (*x*_{1}, *x*_{2}, *x*_{3}), multiplied by the square root of the number of dimensions of the vector (3 in this case).

An observation is rarely more than a few standard deviations away from the mean. Chebyshev's inequality ensures that, for all distributions for which the standard deviation is defined, the amount of data within a number of standard deviations of the mean is at least as much as given in the following table.

Distance from mean | Minimum population |
---|---|

50% | |

2σ | 75% |

3σ | 89% |

4σ | 94% |

5σ | 96% |

6σ | 97% |

^{ [17] } | |

The central limit theorem states that the distribution of an average of many independent, identically distributed random variables tends toward the famous bell-shaped normal distribution with a probability density function of

where *μ* is the expected value of the random variables, *σ* equals their distribution's standard deviation divided by *n*^{1/2}, and *n* is the number of random variables. The standard deviation therefore is simply a scaling variable that adjusts how broad the curve will be, though it also appears in the normalizing constant.

If a data distribution is approximately normal, then the proportion of data values within *z* standard deviations of the mean is defined by:

where is the error function. The proportion that is less than or equal to a number, *x*, is given by the cumulative distribution function:

- .
^{ [18] }

If a data distribution is approximately normal then about 68 percent of the data values are within one standard deviation of the mean (mathematically, *μ* ± *σ*, where *μ* is the arithmetic mean), about 95 percent are within two standard deviations (*μ* ± 2*σ*), and about 99.7 percent lie within three standard deviations (*μ* ± 3*σ*). This is known as the * 68-95-99.7 rule *, or *the empirical rule*.

For various values of *z*, the percentage of values expected to lie in and outside the symmetric interval, CI = (−*zσ*, *zσ*), are as follows:

Confidence interval | Proportion within | Proportion without | |
---|---|---|---|

Percentage | Percentage | Fraction | |

0.318639σ | 25% | 75% | 3 / 4 |

0.674490σ | 50% | 50% | 1 / 2 |

0.977925σ | 66.6% | 33.3% | 1 / 3 |

0.994458σ | 68% | 32% | 1 / 3.125 |

1σ | 68.2689492% | 31.7310508% | 1 / 3.1514872 |

1.281552σ | 80% | 20% | 1 / 5 |

1.644854σ | 90% | 10% | 1 / 10 |

1.959964σ | 95% | 5% | 1 / 20 |

2σ | 95.4499736% | 4.5500264% | 1 / 21.977895 |

2.575829σ | 99% | 1% | 1 / 100 |

3σ | 99.7300204% | 0.2699796% | 1 / 370.398 |

3.290527σ | 99.9% | 0.1% | 1 / 1000 |

3.890592σ | 99.99% | 0.01% | 1 / 10000 |

4σ | 99.993666% | 0.006334% | 1 / 15787 |

4.417173σ | 99.999% | 0.001% | 1 / 100000 |

4.5σ | 99.9993204653751% | 0.0006795346249% | 1 / 147159.5358 6.8 / 1000000 |

4.891638σ | 99.9999% | 0.0001% | 1 / 1000000 |

5σ | 99.9999426697% | 0.0000573303% | 1 / 1744278 |

5.326724σ | 99.99999% | 0.00001% | 1 / 10000000 |

5.730729σ | 99.999999% | 0.000001% | 1 / 100000000 |

6σ | 99.9999998027% | 0.0000001973% | 1 / 506797346 |

6.109410σ | 99.9999999% | 0.0000001% | 1 / 1000000000 |

6.466951σ | 99.99999999% | 0.00000001% | 1 / 10000000000 |

6.806502σ | 99.999999999% | 0.000000001% | 1 / 100000000000 |

7σ | 99.9999999997440% | 0.000000000256% | 1 / 390682215445 |

The mean and the standard deviation of a set of data are descriptive statistics usually reported together. In a certain sense, the standard deviation is a "natural" measure of statistical dispersion if the center of the data is measured about the mean. This is because the standard deviation from the mean is smaller than from any other point. The precise statement is the following: suppose *x*_{1}, ..., *x*_{n} are real numbers and define the function:

Using calculus or by completing the square, it is possible to show that *σ*(*r*) has a unique minimum at the mean:

Variability can also be measured by the coefficient of variation, which is the ratio of the standard deviation to the mean. It is a dimensionless number.

Often, we want some information about the precision of the mean we obtained. We can obtain this by determining the standard deviation of the sampled mean. Assuming statistical independence of the values in the sample, the standard deviation of the mean is related to the standard deviation of the distribution by:

where *N* is the number of observations in the sample used to estimate the mean. This can easily be proven with (see basic properties of the variance):

(Statistical independence is assumed.)

hence

Resulting in:

In order to estimate the standard deviation of the mean it is necessary to know the standard deviation of the entire population beforehand. However, in most applications this parameter is unknown. For example, if a series of 10 measurements of a previously unknown quantity is performed in a laboratory, it is possible to calculate the resulting sample mean and sample standard deviation, but it is impossible to calculate the standard deviation of the mean.

The following two formulas can represent a running (repeatedly updated) standard deviation. A set of two power sums *s*_{1} and *s*_{2} are computed over a set of *N* values of *x*, denoted as *x*_{1}, ..., *x*_{N}:

Given the results of these running summations, the values *N*, *s*_{1}, *s*_{2} can be used at any time to compute the *current* value of the running standard deviation:

Where N, as mentioned above, is the size of the set of values (or can also be regarded as *s*_{0}).

Similarly for sample standard deviation,

In a computer implementation, as the three *s*_{j} sums become large, we need to consider round-off error, arithmetic overflow, and arithmetic underflow. The method below calculates the running sums method with reduced rounding errors.^{ [19] } This is a "one pass" algorithm for calculating variance of *n* samples without the need to store prior data during the calculation. Applying this method to a time series will result in successive values of standard deviation corresponding to *n* data points as *n* grows larger with each new sample, rather than a constant-width sliding window calculation.

For *k* = 1, ..., *n*:

where A is the mean value.

Note: since or

Sample variance:

Population variance:

When the values *x _{i}* are weighted with unequal weights

And the standard deviation equations remain unchanged. *s*_{0} is now the sum of the weights and not the number of samples *N*.

The incremental method with reduced rounding errors can also be applied, with some additional complexity.

A running sum of weights must be computed for each *k* from 1 to *n*:

and places where 1/*n* is used above must be replaced by *w _{i}*/

In the final division,

and

or

where *n* is the total number of elements, and *n'* is the number of elements with non-zero weights.

The above formulas become equal to the simpler formulas given above if weights are taken as equal to one.

The term *standard deviation* was first used in writing by Karl Pearson in 1894, following his use of it in lectures.^{ [20] }^{ [21] } This was as a replacement for earlier alternative names for the same idea: for example, Gauss used *mean error*.^{ [22] }

In two dimensions, the standard deviation can be illustrated with the standard deviation ellipse, see Multivariate normal distribution § Geometric interpretation.

- 68–95–99.7 rule
- Accuracy and precision
- Chebyshev's inequality An inequality on location and scale parameters
- Coefficient of variation
- Cumulant
- Deviation (statistics)
- Distance correlation Distance standard deviation
- Error bar
- Geometric standard deviation
- Mahalanobis distance generalizing number of standard deviations to the mean
- Mean absolute error
- Pooled variance
- Propagation of uncertainty
- Percentile
- Raw data
- Robust standard deviation
- Root mean square
- Sample size
- Samuelson's inequality
- Six Sigma
- Standard error
- Standard score
- Volatility (finance)
- Yamartino method for calculating standard deviation of wind direction

In probability theory and statistics, **kurtosis** is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Like skewness, kurtosis describes the shape of a probability distribution and there are different ways of quantifying it for a theoretical distribution and corresponding ways of estimating it from a sample from a population. Different measures of kurtosis may have different interpretations.

In probability theory, a **normal****distribution** is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is

In probability theory and statistics, **skewness** is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive, zero, negative, or undefined.

In probability theory and statistics, **variance** is the expectation of the squared deviation of a random variable from its mean. Informally, it measures how far a set of numbers is spread out from their average value. Variance has a central role in statistics, where some ideas that use it include descriptive statistics, statistical inference, hypothesis testing, goodness of fit, and Monte Carlo sampling. Variance is an important tool in the sciences, where statistical analysis of data is common. The variance is the square of the standard deviation, the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by , , or .

The **weighted arithmetic mean** is similar to an ordinary arithmetic mean, except that instead of each of the data points contributing equally to the final average, some data points contribute more than others. The notion of weighted mean plays a role in descriptive statistics and also occurs in a more general form in several other areas of mathematics.

In probability theory, a **log-normal distribution** is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable X is log-normally distributed, then *Y* = ln(*X*) has a normal distribution. Equivalently, if Y has a normal distribution, then the exponential function of Y, *X* = exp(*Y*), has a log-normal distribution. A random variable which is log-normally distributed takes only positive real values. It is a convenient and useful model for measurements in exact and engineering sciences, as well as medicine, economics and other topics.

In probability and statistics, **Student's t-distribution** is any member of a family of continuous probability distributions that arises when estimating the mean of a normally distributed population in situations where the sample size is small and the population standard deviation is unknown. It was developed by William Sealy Gosset under the pseudonym

In probability theory, **Chebyshev's inequality** guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean. Specifically, no more than 1/*k*^{2} of the distribution's values can be more than *k* standard deviations away from the mean. The rule is often called Chebyshev's theorem, about the range of standard deviations around the mean, in statistics. The inequality has great utility because it can be applied to any probability distribution in which the mean and variance are defined. For example, it can be used to prove the weak law of large numbers.

In statistics, the **mean squared error** (**MSE**) or **mean squared deviation** (**MSD**) of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. That MSE is almost always strictly positive can be attributed to either randomness, or the fact that the estimator does not account for information that could produce a more accurate estimate.

In statistics and optimization, **errors** and **residuals** are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "theoretical value". The **error** of an observed value is the deviation of the observed value from the (unobservable) *true* value of a quantity of interest, and the **residual** of an observed value is the difference between the observed value and the *estimated* value of the quantity of interest. The distinction is most important in regression analysis, where the concepts are sometimes called the **regression errors** and **regression residuals** and where they lead to the concept of studentized residuals.

The **standard error** (**SE**) of a statistic is the standard deviation of its sampling distribution or an estimate of that standard deviation. If the statistic is the sample mean, it is called the **standard error of the mean** (**SEM**).

**Directional statistics** is the subdiscipline of statistics that deals with directions, axes or rotations in **R**^{n}. More generally, directional statistics deals with observations on compact Riemannian manifolds.

In statistics, a **consistent estimator** or **asymptotically consistent estimator** is an estimator—a rule for computing estimates of a parameter *θ*_{0}—having the property that as the number of data points used increases indefinitely, the resulting sequence of estimates converges in probability to *θ*_{0}. This means that the distributions of the estimates become more and more concentrated near the true value of the parameter being estimated, so that the probability of the estimator being arbitrarily close to *θ*_{0} converges to one.

**Sample size determination** is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complicated studies there may be several different sample sizes: for example, in a stratified survey there would be different sizes for each stratum. In a census, data is sought for an entire population, hence the intended sample size is equal to the population. In experimental design, where a study may be divided into different treatment groups, there may be different sample sizes for each group.

In probability theory, the **Rice distribution** or **Rician distribution** is the probability distribution of the magnitude of a circularly-symmetric bivariate normal random variable, possibly with non-zero mean (noncentral). It was named after Stephen O. Rice.

In statistics, the **bias** of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called **unbiased**. In statistics, "bias" is an **objective** property of an estimator. Bias can also be measured with respect to the median, rather than the mean, in which case one distinguishes *median*-unbiased from the usual *mean*-unbiasedness property. Bias is a distinct concept from consistency. Consistent estimators converge in probability to the true value of the parameter, but may be biased or unbiased; see bias versus consistency for more.

In statistics, the **median absolute deviation** (**MAD**) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample.

In statistics and in particular statistical theory, **unbiased estimation of a standard deviation** is the calculation from a statistical sample of an estimated value of the standard deviation of a population of values, in such a way that the expected value of the calculation equals the true value. Except in some important situations, outlined later, the task has little relevance to applications of statistics since its need is avoided by standard procedures, such as the use of significance tests and confidence intervals, or by using Bayesian analysis.

In statistics, **pooled variance** is a method for estimating variance of several different populations when the mean of each population may be different, but one may assume that the variance of each population is the same. The numerical estimate resulting from the use of this method is also called the pooled variance.

In statistics, **Bessel's correction** is the use of *n* − 1 instead of *n* in the formula for the sample variance and sample standard deviation, where *n* is the number of observations in a sample. This method corrects the bias in the estimation of the population variance. It also partially corrects the bias in the estimation of the population standard deviation. However, the correction often increases the mean squared error in these estimations. This technique is named after Friedrich Bessel.

- ↑ Bland, J.M.; Altman, D.G. (1996). "Statistics notes: measurement error".
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*Math Vault*. 26 April 2020. Retrieved 21 August 2020. - ↑ Gauss, Carl Friedrich (1816). "Bestimmung der Genauigkeit der Beobachtungen".
*Zeitschrift für Astronomie und Verwandte Wissenschaften*.**1**: 187–197. - ↑ Walker, Helen (1931).
*Studies in the History of the Statistical Method*. Baltimore, MD: Williams & Wilkins Co. pp. 24–25. - ↑ Logan, Murray (2010),
*Biostatistical Design and Analysis Using R*(First ed.), Wiley-Blackwell - ↑ Furness, R.W.; Bryant, D.M. (1996). "Effect of wind on field metabolic rates of breeding northern fulmars".
*Ecology*.**77**(4): 1181–1188. doi:10.2307/2265587. JSTOR 2265587. - 1 2 Weisstein, Eric W. "Standard Deviation".
*mathworld.wolfram.com*. Retrieved 21 August 2020. - ↑ Weisstein, Eric W. "Bessel's Correction".
*MathWorld*. - ↑ "Standard Deviation Formulas".
*www.mathsisfun.com*. Retrieved 21 August 2020. - ↑ Gurland, John; Tripathi, Ram C. (1971), "A Simple Approximation for Unbiased Estimation of the Standard Deviation",
*The American Statistician*,**25**(4): 30–32, doi:10.2307/2682923, JSTOR 2682923 - ↑ Shiffler, Ronald E.; Harsha, Phillip D. (1980). "Upper and Lower Bounds for the Sample Standard Deviation".
*Teaching Statistics*.**2**(3): 84–86. doi:10.1111/j.1467-9639.1980.tb00398.x. - ↑ Browne, Richard H. (2001). "Using the Sample Range as a Basis for Calculating Sample Size in Power Calculations".
*The American Statistician*.**55**(4): 293–298. doi:10.1198/000313001753272420. JSTOR 2685690. - ↑ "What does the 5 sigma mean?". Physics.org. Retrieved 5 February 2019.
- ↑ "CERN experiments observe particle consistent with long-sought Higgs boson | CERN press office". Press.web.cern.ch. 4 July 2012. Retrieved 30 May 2015.
- ↑ LIGO Scientific Collaboration, Virgo Collaboration (2016), "Observation of Gravitational Waves from a Binary Black Hole Merger",
*Physical Review Letters*,**116**(6): 061102, arXiv: 1602.03837 , Bibcode:2016PhRvL.116f1102A, doi:10.1103/PhysRevLett.116.061102, PMID 26918975 - ↑ "What is Standard Deviation". Pristine. Retrieved 29 October 2011.
- ↑ Ghahramani, Saeed (2000).
*Fundamentals of Probability*(2nd ed.). New Jersey: Prentice Hall. p. 438. - ↑ Eric W. Weisstein. "Distribution Function". MathWorld—A Wolfram Web Resource. Retrieved 30 September 2014.
- ↑ Welford, BP (August 1962). "Note on a Method for Calculating Corrected Sums of Squares and Products".
*Technometrics*.**4**(3): 419–420. CiteSeerX 10.1.1.302.7503 . doi:10.1080/00401706.1962.10490022. - ↑ Dodge, Yadolah (2003).
*The Oxford Dictionary of Statistical Terms*. Oxford University Press. ISBN 978-0-19-920613-1. - ↑ Pearson, Karl (1894). "On the dissection of asymmetrical frequency curves".
*Philosophical Transactions of the Royal Society A*.**185**: 71–110. Bibcode:1894RSPTA.185...71P. doi: 10.1098/rsta.1894.0003 . - ↑ Miller, Jeff. "Earliest Known Uses of Some of the Words of Mathematics".

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