Student's t-distribution

Student's t
Probability density function
Cumulative distribution function
parameters:	$\nu > 0$ degrees of freedom (real)
support:	$x \in (-\infty; +\infty)\!$
pdf:	$\frac{\Gamma(\frac{\nu+1}{2})} {\sqrt{\nu\pi}\,\Gamma(\frac{\nu}{2})} \left(1+\frac{x^2}{\nu} \right)^{-(\frac{\nu+1}{2})}\!$
cdf:	$\begin{matrix} \frac{1}{2} + x \Gamma \left( \frac{\nu+1}{2} \right) \cdot\\[0.5em] \frac{\,_2F_1 \left ( \frac{1}{2},\frac{\nu+1}{2};\frac{3}{2}; -\frac{x^2}{\nu} \right)} {\sqrt{\pi\nu}\,\Gamma (\frac{\nu}{2})} \end{matrix}$ where ₂F₁ is the hypergeometric function
mean:	$0\text{ for }\nu>1$ , otherwise undefined
median:	$0$
mode:	$0$
variance:	$\frac{\nu}{\nu-2}\text{ for }\nu>2\!$ , $\infty$ for $1<\nu\le2$ , otherwise undefined
skewness:	$0\text{ for }\nu>3$
ex.kurtosis:	$\frac{6}{\nu-4}\text{ for }\nu>4\!$
entropy:	$\begin{matrix} \frac{\nu+1}{2}\left[ \psi(\frac{1+\nu}{2}) - \psi(\frac{\nu}{2}) \right] \\[0.5em] + \log{\left[\sqrt{\nu}B(\frac{\nu}{2},\frac{1}{2})\right]} \end{matrix}$ $\psi$ : digamma function, $B$ : beta function
mgf:	(Not defined)
cf:	$\frac{K_{\nu/2}(\sqrt{\nu}\|t\|)(\sqrt{\nu}\|t\|)^{\nu/2}}{\Gamma(\nu/2)2^{\nu/2-1}},\;\nu>0$ $K_{\nu}(x)$ : Bessel function^[1]

In probability and statistics, Student's t-distribution (or simply the t-distribution) is a continuous probability distribution that arises in the problem of estimating the mean of a normally distributed population when the sample size is small. It is the basis of the popular Student's t-tests for the statistical significance of the difference between two sample means, and for confidence intervals for the difference between two population means. The Student's t-distribution also arises in the Bayesian analysis of data from a normal family. The Student's t-distribution is a special case of the generalised hyperbolic distribution.

In statistics, the t-distribution was first derived as a posterior distribution by Helmert and Lüroth.^[2]^[3]^[4] In the English literature, a derivation of the t-distribution was published in 1908 by William Sealy Gosset^[5] while he worked at the Guinness Brewery in Dublin. Due to proprietary issues, the paper was written under the pseudonym Student. The t-test and the associated theory became well-known through the work of R.A. Fisher, who called the distribution "Student's distribution".^[6]

Student's distribution arises when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data. Quite often, however, textbook problems will treat the population standard deviation as if it were known and thereby avoid the need to use the Student's t-test. These problems are generally of two kinds: (1) those in which the sample size is so large that one may treat a data-based estimate of the variance as if it were certain, and (2) those that illustrate mathematical reasoning, in which the problem of estimating the standard deviation is temporarily ignored because that is not the point that the author or instructor is then explaining.

Etymology

The "Student's" distribution was actually published in 1908 by William Sealy Gosset. Gosset, however, was employed at a brewery that forbade members of its staff publishing scientific papers due to an earlier paper containing trade secrets. To circumvent this restriction, Gosset used the name "Student", and consequently the distribution was named "Student's t-distribution".^[7]

Characterization

Student's t-distribution is the probability distribution of the ratio^[8]

$\frac{Z}{\sqrt{V/\nu\ }} = Z \sqrt{\nu / V}$

where

Z is normally distributed with expected value 0 and variance 1;
V has a chi-square distribution with $\nu$ degrees of freedom;
Z and V are independent.

While, for any given constant μ, $\frac{Z+\mu}{\sqrt{V/\nu\ }}$ is a random variable of noncentral t-distribution with noncentrality parameter μ.

Probability density function

Student's t-distribution has the probability density function

$f(t) = \frac{\Gamma(\frac{\nu+1}{2})} {\sqrt{\nu\pi}\,\Gamma(\frac{\nu}{2})} \left(1+\frac{t^2}{\nu} \right)^{-(\nu+1)/2},\!$

where $\nu$ is the number of degrees of freedom and $\Gamma$ is the Gamma function.

For $\nu$ even,

$\frac{\Gamma(\frac{\nu+1}{2})} {\sqrt{\nu\pi}\,\Gamma(\frac{\nu}{2})} = \frac{(\nu -1)(\nu -3)\cdots 5 \cdot 3} {2\sqrt{\nu}(\nu -2)(\nu -4)\cdots 4 \cdot 2\,}.$

For $\nu$ odd,

$\frac{\Gamma(\frac{\nu+1}{2})} {\sqrt{\nu\pi}\,\Gamma(\frac{\nu}{2})} = \frac{(\nu -1)(\nu -3)\cdots 4 \cdot 2} {\pi \sqrt{\nu}(\nu -2)(\nu -4)\cdots 5 \cdot 3\,}.\!$

The overall shape of the probability density function of the t-distribution resembles the bell shape of a normally distributed variable with mean 0 and variance 1, except that it is a bit lower and wider. As the number of degrees of freedom grows, the t-distribution approaches the normal distribution with mean 0 and variance 1.

The following images show the density of the t-distribution for increasing values of $\nu$ . The normal distribution is shown as a blue line for comparison. Note that the t-distribution (red line) becomes closer to the normal distribution as $\nu$ increases.

Density of the t-distribution (red) for 1, 2, 3, 5, 10, and 30 df compared to normal distribution (blue). Previous plots shown in green.
1 degree of freedom	2 degrees of freedom	3 degrees of freedom
5 degrees of freedom	10 degrees of freedom	30 degrees of freedom

Derivation

Suppose X₁, ..., X_n are independent values that are normally distributed with expected value μ and variance σ². Let

$\overline{X}_n = (X_1+\cdots+X_n)/n$

be the sample mean, and

$S_n^{\;2} = \frac{1}{n-1}\sum_{i=1}^n\left(X_i-\overline{X}_n\right)^2$

be the sample variance. It can be shown that the random variable

$\frac{(n-1)S_n^2}{\sigma^2}$

has a chi-square distribution with n − 1 degrees of freedom (by Cochran's theorem). It is readily shown that the quantity

$Z=\frac{\overline{X}_n-\mu}{\sigma/\sqrt{n}}$

is normally distributed with mean 0 and variance 1, since the sample mean $\scriptstyle \overline{X}_n$ is normally distributed with mean $\mu$ and standard error $\scriptstyle\sigma/\sqrt{n}$ . Moreover, it is possible to show that these two random variables—the normally distributed one and the chi-square-distributed one—are independent. Consequently the pivotal quantity,

$T=\frac{\overline{X}_n-\mu}{S_n / \sqrt{n}},$

which differs from Z in that the exact standard deviation $\scriptstyle \sigma$ is replaced by the random variable $\scriptstyle S_n$ , has a Student's t-distribution as defined above. Notice that the unknown population variance σ² does not appear in T, since it was in both the numerator and the denominators, so it canceled. Gosset's work showed that T has the probability density function

$f(t) = \frac{\Gamma(\frac{\nu+1}{2})} {\sqrt{\nu\pi}\,\Gamma(\frac{\nu}{2})} \left(1+\frac{t^2}{\nu} \right)^{-(\frac{\nu+1}{2})}\!,$

with $\nu$ equal to n − 1.

This may also be written as

$f(t) = \frac{1}{\sqrt{\nu}\, B \left (\frac{1}{2}, \frac{\nu}{2}\right )} \left(1+\frac{t^2}{\nu} \right)^{-(\frac{\nu+1}{2})}\!,$

where B is the Beta function.

The distribution of T is now called the t-distribution. The parameter $\nu$ is called the number of degrees of freedom. The distribution depends on $\nu$ , but not μ or σ; the lack of dependence on μ and σ is what makes the t-distribution important in both theory and practice.

Gosset's result can be stated more generally. (See, for example, Hogg and Craig, Sections 4.4 and 4.8.) Let Z have a normal distribution with mean 0 and variance 1. Let V have a chi-square distribution with $\nu$ degrees of freedom. Further suppose that Z and V are independent (see Cochran's theorem). Then the ratio

$\frac{Z}{\sqrt{V/\nu\ }}$

has a t-distribution with $\nu$ degrees of freedom.

Cumulative distribution function

The cumulative distribution function is given by the regularized incomplete beta function,

$\int_{-\infty}^t f(u)\,du = I_x\left(\frac{\nu}{2},\frac{\nu}{2}\right) = \frac{B\left(x;\frac{\nu}{2},\frac{\nu}{2}\right)}{B\left(\frac{\nu}{2},\frac{\nu}{2}\right)}$

with

$x = \frac{t+\sqrt{t^2+\nu}}{2\sqrt{t^2+\nu}}.$

Properties

Moments

The moments of the t-distribution are

$E(T^k)=\begin{cases} 0 & k \text{ odd},\quad 0<k< \nu\\ \frac{\Gamma(\frac{k+1}{2})\Gamma(\frac{\nu-k}{2})\nu^{k/2}}{\sqrt{\pi}\Gamma(\frac{\nu}{2})} & k \text{ even}, \quad 0<k< \nu\\ \text{undefined} & k \text{ odd},\quad 0<\nu\leq k\\ \infty & k\text{ even},\quad 0<\nu\leq k \end{cases}$

It should be noted that the term for 0 < k < $\nu$ , k even, may be simplified using the properties of the Gamma function to

$E(T^k)= \prod_{i=1}^{k/2} \frac{2i-1}{\nu - 2i}\nu^{k/2} \qquad k\text{ even},\quad 0<k<\nu.$

For a t-distribution with $\nu$ degrees of freedom, the expected value is 0, and its variance is $\nu$ /( $\nu$ − 2) if $\nu$ > 2. The skewness is 0 if $\nu$ > 3 and the excess kurtosis is 6/( $\nu$ − 4) if $\nu$ > 4.

Confidence intervals

Suppose the number A is so chosen that

$\Pr(-A < T < A)=0.9,\,$

when T has a t-distribution with n − 1 degrees of freedom. By symmetry, this is the same as saying that A satisfies

$\Pr(T < A) = 0.95,\,$

so A is the "95th percentile" of this probability distribution, or $A=t_{(0.05,n-1)}$ . Then

$\Pr \left (-A < {\overline{X}_n - \mu \over S_n/\sqrt{n}} < A \right)=0.9,$

and this is equivalent to

$\Pr\left(\overline{X}_n - A{S_n \over \sqrt{n}} < \mu < \overline{X}_n + A{S_n \over \sqrt{n}}\right) = 0.9.$

Therefore the interval whose endpoints are

$\overline{X}_n\pm A\frac{S_n}{\sqrt{n}}$

is a 90-percent confidence interval for μ. Therefore, if we find the mean of a set of observations that we can reasonably expect to have a normal distribution, we can use the t-distribution to examine whether the confidence limits on that mean include some theoretically predicted value - such as the value predicted on a null hypothesis.

It is this result that is used in the Student's t-tests: since the difference between the means of samples from two normal distributions is itself distributed normally, the t-distribution can be used to examine whether that difference can reasonably be supposed to be zero.

If the data are normally distributed, the one-sided (1 − a)-upper confidence limit (UCL) of the mean, can be calculated using the following equation:

$\mathrm{UCL}_{1-a} = \overline{X}_n+\frac{t_{a,n-1} S_n}{\sqrt{n}}.$

The resulting UCL will be the greatest average value that will occur for a given confidence interval and population size. In other words, $\overline{X}_n$ being the mean of the set of observations, the probability that the mean of the distribution is inferior to UCL_1−a is equal to the confidence level 1 − a.

A number of other statistics can be shown to have t-distributions for samples of moderate size under null hypotheses that are of interest, so that the t-distribution forms the basis for significance tests in other situations as well as when examining the differences between means. For example, the distribution of Spearman's rank correlation coefficient ρ, in the null case (zero correlation) is well approximated by the t distribution for sample sizes above about 20.

Prediction interval

The t-distribution can be used to construct a prediction interval for an unobserved sample from a normal distribution with unknown mean and variance.

Monte Carlo sampling

There are various approaches to constructing random samples from the Student distribution. The matter depends on whether the samples are required on a stand-alone basis, or are to be constructed by application of a quantile function to uniform samples, e.g. in multi-dimensional applications basis on copula-dependency. In the case of stand-alone sampling, Bailey's 1994 extension of the Box-Muller method and its polar variation are easily deployed. It has the merit that it applies equally well to all real positive and negative degrees of freedom.

Integral of Student's probability density function and p-value

The function $\scriptstyle A(t|\nu)$ is the integral of Student's probability density function, ƒ(t) between −t and t. It thus gives the probability that a value of t less than that calculated from observed data would occur by chance. Therefore, the function $\scriptstyle A(t|\nu)$ can be used when testing whether the difference between the means of two sets of data is statistically significant, by calculating the corresponding value of t and the probability of its occurrence if the two sets of data were drawn from the same population. This is used in a variety of situations, particularly in t-tests. For the statistic t, with $\scriptstyle\nu$ degrees of freedom, $\scriptstyle A(t|\nu)$ is the probability that t would be less than the observed value if the two means were the same (provided that the smaller mean is subtracted from the larger, so that t > 0). It is defined for real t by the following formula:

$A(t|\nu) = \Pr \left \{ \left | \frac{Z}{\sqrt{V/\nu}} \right | \le t \right \} = \frac{1}{\sqrt{\nu} B \left (\frac{1}{2}, \frac{\nu}{2}\right )} \int\limits_{-t}^t \left (1+\frac{x^2}{\nu}\right )^{-\frac{\nu +1}{2} }\, dx$

where B is the Beta function. For t > 0, there is a relation to the regularized incomplete beta function I_x(a, b) as follows:

$A(t|\nu) = 1 - I_{\frac{\nu}{\nu +t^2}}\left(\frac{\nu}{2},\frac{1}{2}\right).$

For statistical hypothesis testing this function is used to construct the p-value.

Related distributions

$X \sim \mathrm{t}(\nu)$ has a t-distribution if $\sigma^2 \sim \mbox{Inv-}\chi^2(\nu,1)\!$ has a scaled inverse-χ² distribution and $X \sim \mathrm{N}(0,\sigma^2)\!$ has a normal distribution.
$Y \sim \mathrm{F}(\nu_1 = 1, \nu_2 = \nu)$ has an F-distribution if $Y = X^2\!$ and $X \sim \mathrm{t}(\nu)\!$ has a Student's t-distribution.
$Y \sim \mathrm{N}(0,1)\!$ has a normal distribution as $Y = \lim_{\nu \to \infty} X$ where $X \sim \mathrm{t}(\nu)$ .
$X \sim \mathrm{Cauchy}(0,1)$ has a Cauchy distribution if $X \sim \mathrm{t}(\nu = 1)$ .

Special cases

Certain values of $\nu$ give an especially simple form.

ν = 1

Distribution function:

$F(x) = \frac{1}{2} + \frac{1}{\pi}\arctan(x).$

Density function:

$f(x) = \frac{1}{{\pi}(1+x^2)}.$

See Cauchy distribution

ν = 2

Distribution function:

$F(x) = \frac{1}{2}\left[1+\frac{x}{\sqrt{2+x^2}}\right].$

Density function:

$f(x) = \frac{1}{\left(2+x^2\right)^{3/2}}.$

Occurrences

Hypothesis testing

Confidence intervals and hypothesis tests rely on Student's t-distribution to cope with uncertainty resulting from estimating the standard deviation from a sample, whereas if the population standard deviation were known, a normal distribution would be used.

Robust parametric modeling

The t-distribution is often used as an alternative to the normal distribution as a model for data.^[9] It is frequently the case that real data have heavier tails than the normal distribution allows for. The classical approach was to identify outliers and exclude or downweight them in some way. However, it is not always easy to identify outliers (especially in high dimensions), and the t-distribution is a natural choice of model for such data and provides a parametric approach to robust statistics.

Lange et al. explored the use of the t-distribution for robust modeling of heavy tailed data in a variety of contexts. A Bayesian account can be found in Gelman et al. The degrees of freedom parameter controls the kurtosis of the distribution and is correlated with the scale parameter. The likelihood can have multiple local maxima and, as such, it is often necessary to fix the degrees of freedom at a fairly low value and estimate the other parameters taking this as given. Some authors report that values between 3 and 9 are often good choices. Venables and Ripley suggest that a value of 5 is often a good choice.

Table of selected values

Most statistical textbooks list t distribution tables. Nowadays, the better way to a fully precise critical t value or a cumulative probability is the statistical function implemented in spreadsheets (Office Excel, OpenOffice Calc, etc.), or an interactive calculating web page. The relevant spreadsheet functions are TDIST and TINV, while online calculating pages save troubles like positions of parameters or names of functions. For example, a Mediawiki page supported by R extension can easily give the interactive result of critical values or cumulative probability, even for noncentral t-distribution.

The following table lists a few selected values for t-distributions with $\nu$ degrees of freedom for a range of one-sided or two-sided critical regions. For an example of how to read this table, take the fourth row, which begins with 4; that means $\nu$ , the number of degrees of freedom, is 4 (and if we are dealing, as above, with n values with a fixed sum, n = 5). Take the fifth entry, in the column headed 95% for one-sided (90% for two-sided). The value of that entry is "2.132". Then the probability that T is less than 2.132 is 95% or Pr(−∞ < T < 2.132) = 0.95; or mean that Pr(−2.132 < T < 2.132) = 0.9.

This can be calculated by the symmetry of the distribution,

Pr(T < −2.132) = 1 − Pr(T > −2.132) = 1 − 0.95 = 0.05,

and so

Pr(−2.132 < T < 2.132) = 1 − 2(0.05) = 0.9.

Note that the last row also gives critical points: a t-distribution with infinitely-many degrees of freedom is a normal distribution. (See above: Related distributions).

One Sided	75%	80%	85%	90%	95%	97.5%	99%	99.5%	99.75%	99.9%	99.95%
Two Sided	50%	60%	70%	80%	90%	95%	98%	99%	99.5%	99.8%	99.9%
1	1.000	1.376	1.963	3.078	6.314	12.71	31.82	63.66	127.3	318.3	636.6
2	0.816	1.061	1.386	1.886	2.920	4.303	6.965	9.925	14.09	22.33	31.60
3	0.765	0.978	1.250	1.638	2.353	3.182	4.541	5.841	7.453	10.21	12.92
4	0.741	0.941	1.190	1.533	2.132	2.776	3.747	4.604	5.598	7.173	8.610
5	0.727	0.920	1.156	1.476	2.015	2.571	3.365	4.032	4.773	5.893	6.869
6	0.718	0.906	1.134	1.440	1.943	2.447	3.143	3.707	4.317	5.208	5.959
7	0.711	0.896	1.119	1.415	1.895	2.365	2.998	3.499	4.029	4.785	5.408
8	0.706	0.889	1.108	1.397	1.860	2.306	2.896	3.355	3.833	4.501	5.041
9	0.703	0.883	1.100	1.383	1.833	2.262	2.821	3.250	3.690	4.297	4.781
10	0.700	0.879	1.093	1.372	1.812	2.228	2.764	3.169	3.581	4.144	4.587
11	0.697	0.876	1.088	1.363	1.796	2.201	2.718	3.106	3.497	4.025	4.437
12	0.695	0.873	1.083	1.356	1.782	2.179	2.681	3.055	3.428	3.930	4.318
13	0.694	0.870	1.079	1.350	1.771	2.160	2.650	3.012	3.372	3.852	4.221
14	0.692	0.868	1.076	1.345	1.761	2.145	2.624	2.977	3.326	3.787	4.140
15	0.691	0.866	1.074	1.341	1.753	2.131	2.602	2.947	3.286	3.733	4.073
16	0.690	0.865	1.071	1.337	1.746	2.120	2.583	2.921	3.252	3.686	4.015
17	0.689	0.863	1.069	1.333	1.740	2.110	2.567	2.898	3.222	3.646	3.965
18	0.688	0.862	1.067	1.330	1.734	2.101	2.552	2.878	3.197	3.610	3.922
19	0.688	0.861	1.066	1.328	1.729	2.093	2.539	2.861	3.174	3.579	3.883
20	0.687	0.860	1.064	1.325	1.725	2.086	2.528	2.845	3.153	3.552	3.850
21	0.686	0.859	1.063	1.323	1.721	2.080	2.518	2.831	3.135	3.527	3.819
22	0.686	0.858	1.061	1.321	1.717	2.074	2.508	2.819	3.119	3.505	3.792
23	0.685	0.858	1.060	1.319	1.714	2.069	2.500	2.807	3.104	3.485	3.767
24	0.685	0.857	1.059	1.318	1.711	2.064	2.492	2.797	3.091	3.467	3.745
25	0.684	0.856	1.058	1.316	1.708	2.060	2.485	2.787	3.078	3.450	3.725
26	0.684	0.856	1.058	1.315	1.706	2.056	2.479	2.779	3.067	3.435	3.707
27	0.684	0.855	1.057	1.314	1.703	2.052	2.473	2.771	3.057	3.421	3.690
28	0.683	0.855	1.056	1.313	1.701	2.048	2.467	2.763	3.047	3.408	3.674
29	0.683	0.854	1.055	1.311	1.699	2.045	2.462	2.756	3.038	3.396	3.659
30	0.683	0.854	1.055	1.310	1.697	2.042	2.457	2.750	3.030	3.385	3.646
40	0.681	0.851	1.050	1.303	1.684	2.021	2.423	2.704	2.971	3.307	3.551
50	0.679	0.849	1.047	1.299	1.676	2.009	2.403	2.678	2.937	3.261	3.496
60	0.679	0.848	1.045	1.296	1.671	2.000	2.390	2.660	2.915	3.232	3.460
80	0.678	0.846	1.043	1.292	1.664	1.990	2.374	2.639	2.887	3.195	3.416
100	0.677	0.845	1.042	1.290	1.660	1.984	2.364	2.626	2.871	3.174	3.390
120	0.677	0.845	1.041	1.289	1.658	1.980	2.358	2.617	2.860	3.160	3.373
$\infty$	0.674	0.842	1.036	1.282	1.645	1.960	2.326	2.576	2.807	3.090	3.291

The number at the beginning of each row in the table above is $\nu$ which has been defined above as n − 1. The percentage along the top is 100%(1 − α). The numbers in the main body of the table are t_α,. If a quantity T is distributed as a Student's t distribution with $\nu$ degrees of freedom, then there is a probability 1 − α that T will be less than t_α,.(Calculated as for a one-tailed or one-sided test as opposed to a two-tailed test.)

For example, given a sample with a sample variance 2 and sample mean of 10, taken from a sample set of 11 (10 degrees of freedom), using the formula

$\overline{X}_n\pm A\frac{S_n}{\sqrt{n}}.$

We can determine that at 90% confidence, we have a true mean lying below

$10+1.37218 \frac{\sqrt{2}}{\sqrt{11}}=10.58510.$

(In other words, on average, 90% of the times that an upper threshold is calculated by this method, the true mean lies below this upper threshold.) And, still at 90% confidence, we have a true mean lying over

$10-1.37218 \frac{\sqrt{2}}{\sqrt{11}}=9.41490.$

(In other words, on average, 90% of the times that a lower threshold is calculated by this method, the true mean lies above this lower threshold.) So that at 80% confidence, we have a true mean lying within the interval

$\left(10-1.37218 \frac{\sqrt{2}}{\sqrt{11}}, 10+1.37218 \frac{\sqrt{2}}{\sqrt{11}}\right) = \left(9.41490, 10.58510\right).$

This is generally expressed in interval notation, e.g., for this case, at 80% confidence the true mean is within the interval [9.41490, 10.58510].

(In other words, on average, 80% of the times that upper and lower thresholds are calculated by this method, the true mean is both below the upper threshold and above the lower threshold. This is not the same thing as saying that there is an 80% probability that the true mean lies between a particular pair of upper and lower thresholds that have been calculated by this method—see confidence interval and prosecutor's fallacy.)

For information on the inverse cumulative distribution function see Quantile function.

Notes

↑ Hurst, Simon, The Characteristic Function of the Student-t Distribution, Financial Mathematics Research Report No. FMRR006-95, Statistics Research Report No. SRR044-95
↑ Lüroth, J (1876). "Vergleichung von zwei Werten des wahrscheinlichen Fehlers". Astron. Nachr. 87: 209–20.
↑ Pfanzagl, J.; Sheynin, O. (1996). "A forerunner of the t-distribution (Studies in the history of probability and statistics XLIV)". Biometrika 83 (4): pp. 891–898. doi:10.1093/biomet/83.4.891. MR 1766040. http://biomet.oxfordjournals.org/cgi/content/abstract/83/4/891.
↑ Sheynin, O (1995). "Helmert's work in the theory of errors". Arch. Hist. Ex. Sci. 49: 73-104.
↑ Student [William Sealy Gosset] (March 1908). "The probable error of a mean". Biometrika 6 (1): 1–25. doi:10.1093/biomet/6.1.1. http://www.york.ac.uk/depts/maths/histstat/student.pdf.
↑ Fisher, R. A. (1925). "Applications of "Student's" distribution". Metron 5: 90–104. http://digital.library.adelaide.edu.au/coll/special/fisher/43.pdf.
↑ Walpole, Ronald; Myers, Raymond; Ye, Keying. Probability and Statistics for Engineers and Scientists. Pearson Education, 2002, 7th edition, pg. 237
↑ Johnson, N.L., Kotz, S., Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 2, 2nd Edition. Wiley, ISBN 0-471-58494-0 (Chapter 28)
↑ Lange, Kenneth L.; Little, Roderick J.A.; Taylor, Jeremy M.G. (1989). "Robust statistical modeling using the t-distribution". JASA 84 (408): 881–896. http://www.jstor.org/stable/2290063.

References

Helmert, F. R. (1875). Über die Bestimmung des wahrscheinlichen Fehlers aus einer endlichen Anzahl wahrer Beobachtungsfehler. Z. Math. Phys. 20, 300-3.
Helmert, F. R. (1876a). Über die Wahrscheinlichkeit der Potenzsummen der Beobachtungsfehler und uber einige damit in Zusammenhang stehende Fragen. Z. Math. Phys. 21, 192-218.
Helmert, F. R. (1876b). Die Genauigkeit der Formel von Peters zur Berechnung des wahrscheinlichen Beobachtungsfehlers directer Beobachtungen gleicher Genauigkeit Astron. Nachr. 88, 113-32.
Senn, S. & Richardson, W. (1994). The first t-test. Statist. Med. 13, 785-803.
Abramowitz, Milton; Stegun, Irene A., eds. (1965), "Chapter 26", Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, New York: Dover, pp. 948, MR 0167642, ISBN 978-0486612720, http://www.math.sfu.ca/~cbm/aands/page_948.htm .
R.V. Hogg and A.T. Craig (1978). Introduction to Mathematical Statistics. New York: Macmillan.
Press, William H.; Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery (1992). Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press. pp. pp. 228–229. ISBN 0-521-43108-5. http://www.nr.com/.
Bailey, R. W. (1994). Polar generation of random variates with the t-distribution. Mathematics of Computation 62(206), 779–781.
W.N. Venables and B.D. Ripley, Modern Applied Statistics with S (Fourth Edition), Springer, 2002
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External links

Comparison of noncentral and central t-distributions Density plot, critical value, cumulative probability, etc., noncentral t-distribution online calculator, based on R platform embedded in Mediawiki
VassarStats Density plot, critical values, etc., calculated for a user-specified number of d.f.
Earliest Known Uses of Some of the Words of Mathematics (S) (Remarks on the history of the term "Student's distribution")
Cumulative distribution function (CDF) calculator for the Student t-distribution
Probability density function (PDF) calculator for the Student t-distribution

Probability distributions

Discrete univariate with finite support

Benford · Bernoulli · Beta-binomial · binomial · categorical · hypergeometric · Poisson binomial · Rademacher · discrete uniform · Zipf · Zipf-Mandelbrot

Discrete univariate with infinite support

Boltzmann · Conway–Maxwell–Poisson · discrete phase-type · extended negative binomial · Gauss–Kuzmin · geometric · logarithmic · negative binomial · parabolic fractal · Poisson · Skellam · Yule–Simon · zeta

Continuous univariate supported on a bounded interval, e.g. [0,1]

Beta · Irwin–Hall · Kumaraswamy · logit-normal · raised cosine · triangular · U-quadratic · uniform · Wigner semicircle

Continuous univariate supported on a semi-infinite interval, usually [0,∞)

Beta prime · Bose–Einstein · Burr · chi-square · chi · Coxian · Erlang · exponential · F · Fermi–Dirac · folded normal · Fréchet · Gamma · generalized extreme value · generalized inverse Gaussian · half-logistic · half-normal · Hotelling's T-square · hyper-exponential · hypoexponential · inverse chi-square (scaled inverse chi-square) · inverse Gaussian · inverse gamma · Lévy · log-normal · log-logistic · Maxwell–Boltzmann · Maxwell speed · Nakagami · noncentral chi-square · Pareto · phase-type · Rayleigh · relativistic Breit–Wigner · Rice · Rosin–Rammler · shifted Gompertz · truncated normal · type-2 Gumbel · Weibull · Wilks' lambda

Continuous univariate supported on the whole real line (−∞, ∞)

Cauchy · extreme value · exponential power · Fisher's z · generalized normal · generalized hyperbolic · Gumbel · hyperbolic secant · Landau · Laplace · logistic · noncentral t · normal (Gaussian) · normal-inverse Gaussian · skew normal · slash · stable · Student's t · type-1 Gumbel · Variance-Gamma · Voigt

Multivariate (joint)

Discrete: Ewens · multinomial · multivariate Polya · negative multinomial Continuous: Dirichlet · Generalized Dirichlet · multivariate normal · multivariate Student · normal-scaled inverse gamma · normal-gamma Matrix-valued: inverse-Wishart · matrix normal · Wishart

Directional, degenerate, and singular

Directional:Circular Uniform · bivariate von Mises · Kent · univariate von Mises · von Mises–Fisher · Wrapped normal · Wrapped Cauchy · Wrapped Lévy Degenerate: discrete degenerate · Dirac delta function Singular: Cantor

Families

Circular · compound Poisson · elliptical · exponential · natural exponential · location-scale · maximum entropy · mixture · Pearson · Tweedie

Some common univariate probability distributions

Continuous	beta • Cauchy • chi-square • exponential • F • gamma • Laplace • log-normal • normal • Pareto • Student's t • uniform • Weibull

Discrete	Bernoulli • binomial • discrete uniform • geometric • hypergeometric • negative binomial • Poisson

List of probability distributions

Statistics

Descriptive statistics

Continuous data

Location	Mean (Arithmetic, Geometric, Harmonic) · Median · Mode

Dispersion	Range · Standard deviation · Coefficient of variation · Percentile · Interquartile range

Shape	Variance · Skewness · Kurtosis · Moments · L-moments

Count data

Index of dispersion

Summary tables

Grouped data · Frequency distribution · Contingency table

Dependence

Pearson product-moment correlation · Rank correlation (Spearman's rho, Kendall's tau) · Partial correlation · Scatter plot

Statistical graphics

Bar chart · Biplot · Box plot · Control chart · Correlogram · Forest plot · Histogram · Q-Q plot · Run chart · Scatter plot · Stemplot · Radar chart

Data collection

Designing studies	Effect size · Standard error · Statistical power · Sample size determination

Survey methodology	Sampling · Stratified sampling · Opinion poll · Questionnaire

Controlled experiment	Design of experiments · Randomized experiment · Random assignment · Replication · Blocking · Regression discontinuity · Optimal design

Uncontrolled studies	Natural experiment · Quasi-experiment · Observational study

Statistical inference

Bayesian inference	Bayesian probability · Prior · Posterior · Credible interval · Bayes factor · Bayesian estimator · Maximum posterior estimator

Frequentist inference	Confidence interval · Hypothesis testing · Sampling distribution · Meta-analysis

Specific tests	Z-test (normal) · Student's t-test · F-test · Chi-square test · Pearson's chi-square · Wald test · Mann–Whitney U · Shapiro–Wilk · Signed-rank · Likelihood-ratio

General estimation	Mean-unbiased · Median-unbiased · Maximum likelihood · Method of moments · Minimum distance · Maximum spacing · Density estimation

Correlation and regression analysis

Correlation	Pearson product-moment correlation · Partial correlation · Confounding variable · Coefficient of determination

Regression analysis	Errors and residuals · Regression model validation · Mixed effects models · Simultaneous equations models

Linear regression	Simple linear regression · Ordinary least squares · General linear model · Bayesian regression

Non-standard predictors	Nonlinear regression · Nonparametric · Semiparametric · Isotonic · Robust

Generalized linear model	Exponential families · Logistic (Bernoulli) · Binomial · Poisson

Formal analyses	Analysis of variance (ANOVA) · Analysis of covariance · Multivariate ANOVA

Data analyses and models for other specific data types


Multivariate statistics	Multivariate regression · Principal components · Factor analysis · Cluster analysis · Copulas

Time series analysis	Decomposition · Trend estimation · Box–Jenkins · ARMA models · Spectral density estimation

Survival analysis	Survival function · Kaplan–Meier · Logrank test · Failure rate · Proportional hazards models · Accelerated failure time model

Categorical data	McNemar's test · Cohen's kappa

Applications

Engineering statistics	Methods engineering · Probabilistic design · Process & Quality control · Reliability · System identification

Environmental statistics	Geostatistics · Climatology

Medical statistics	Epidemiology · Clinical trial · Clinical study design

Social statistics	Actuarial science · Population · Demography · Census · Psychometrics · Official statistics · Crime statistics

Category · Portal · Outline · Index