Hotelling's T-squared distribution

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In statistics Hotelling's T-squared distribution is a univariate distribution proportional to the F-distribution and arises importantly as the distribution of a set of statistics which are natural generalizations of the statistics underlying Student's t-distribution. In particular, the distribution arises in multivariate statistics in undertaking tests of the differences between the (multivariate) means of different populations, where tests for univariate problems would make use of a t-test. The distribution is named for Harold Hotelling, who developed it[1] as a generalization of Student's t-distribution.


If the vector pd1 is Gaussian multivariate-distributed with zero mean and unit covariance matrix N(p01,pIp) and pMp is a p x p matrix with unit scale matrix and m degrees of freedom with a Wishart distribution W(pIp,m), then the Quadratic form m(1dT p M−1pd1) has a Hotelling T2(p,m) distribution with dimensionality parameter p and m degrees of freedom.[2]

If a random variable X has Hotelling's T-squared distribution, , then:[1]

where is the F-distribution with parameters p and m−p+1.


Hotelling's t-squared statistic is a generalization of Student's t statistic that is used in multivariate hypothesis testing.[1] The definition follows after it is motivated using a simpler problem.


Let denote a p-variate normal distribution with location and known covariance . Let

be n independent random variables, which may be represented as column vectors of real numbers. Define

to be the sample mean with covariance . It can be shown that

where is the chi-squared distribution with p degrees of freedom.


The covariance matrix used above is often unknown. Here we use instead the sample covariance:

where we denote transpose by an apostrophe. It can be shown that is a positive (semi) definite matrix and follows a p-variate Wishart distribution with n−1 degrees of freedom.[3] The sample covariance matrix of the mean reads .

Hotelling's t-squared statistic is then defined as:[4]

Also, from the #Distribution,

where is the F-distribution with parameters p and n − p. In order to calculate a p-value (unrelated to the p variable here), divide the t2 statistic by the above fraction and use the F-distribution.

Two-sample statistic[edit]

If and , with the samples independently drawn from two independent multivariate normal distributions with the same mean and covariance, and we define

as the sample means, and

as the respective sample covariance matrices. Then

is the unbiased pooled covariance matrix estimate (an extension of pooled variance).

Finally, the Hotelling's two-sample t-squared statistic is

Related concepts[edit]

It can be related to the F-distribution by[3]

The non-null distribution of this statistic is the noncentral F-distribution (the ratio of a non-central Chi-squared random variable and an independent central Chi-squared random variable)


where is the difference vector between the population means.

In the two-variable case, the formula simplifies nicely allowing appreciation of how the correlation, , between the variables affects . If we define



Thus, if the differences in the two rows of the vector are of the same sign, in general, becomes smaller as becomes more positive. If the differences are of opposite sign becomes larger as becomes more positive.

A univariate special case can be found in Welch's t-test.

More robust and powerful tests than Hotelling's two-sample test have been proposed in the literature, see for example the interpoint distance based tests which can be applied also when the number of variables is comparable with, or even larger than, the number of subjects.[5][6]

See also[edit]


  1. ^ a b c Hotelling, H. (1931). "The generalization of Student's ratio". Annals of Mathematical Statistics. 2 (3): 360–378. doi:10.1214/aoms/1177732979. 
  2. ^ Eric W. Weisstein, MathWorld
  3. ^ a b Mardia, K. V.; Kent, J. T.; Bibby, J. M. (1979). Multivariate Analysis. Academic Press. ISBN 0-12-471250-9. 
  4. ^ [1]
  5. ^ Marozzi, M. (2014). "Multivariate tests based on interpoint distances with application to magnetic resonance imaging". Statistical Methods in Medical Research. doi:10.1177/0962280214529104. 
  6. ^ Marozzi, M. (2015). "Multivariate multidistance tests for high-dimensional low sample size case-control studies". Statistics in Medicine. 34. doi:10.1002/sim.6418. 

External links[edit]