Student's t-test

A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution if the null hypothesis is true. It is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's t distribution.

History

The t-statistic was introduced in 1908 by William Sealy Gosset, a chemist working for the Guinness brewery in Dublin, Ireland ("Student" was his pen name).Gosset had been hired due to Claude Guinness's innovative policy of recruiting the best graduates from Oxford and Cambridge to apply biochemistry and statistics to Guinness' industrial processes. Gosset devised the t-test as a way to cheaply monitor the quality of stout. He published the test in Biometrika in 1908, but was forced to use a pen name by his employer, who regarded the fact that they were using statistics as a trade secret. In fact, Gosset's identity was unknown to fellow statisticians.

Uses

Among the most frequently used t-tests are:

• A one sample location test of whether the mean of a normally distributed population has a value specified in a null hypothesis.

• A two sample location test of the null hypothesis that the means of two normally distributed populations are equal. All such tests are usually called Student's t-tests, though strictly speaking that name should only be used if the variances of the two populations are also assumed to be equal; the form of the test used when this assumption is dropped is sometimes called Welch's t-test. These tests are often referred to as "unpaired" or "independent samples" t-tests, as they are typically applied when the statistical units underlying the two samples being compared are non-overlapping.

• A test of the null hypothesis that the difference between two responses measured on the same statistical unit has a mean value of zero. For example, suppose we measure the size of a cancer patient's tumor before and after a treatment. If the treatment is effective, we expect the tumor size for many of the patients to be smaller following the treatment. This is often referred to as the "paired" or "repeated measures" t-test: see paired difference test.

• A test of whether the slope of a regression line differs significantly from 0.

Assumptions

Most t-test statistics have the form T = Z/s, where Z and s are functions of the data. Typically, Z is designed to be sensitive to the alternative hypothesis (i.e. its magnitude tends to be larger when the alternative hypothesis is true), whereas s is a scaling parameter that allows the distribution of T to be determined.

As an example, in the one-sample t-test Z is ${\sqrt {n}}{\bar {X}}/\sigma$, where ${\bar {X}}$ is the sample mean of the data, n is the sample size, and σ is the population standard deviation of the data; s in the one-sample t-test is ${\hat {\sigma }}/\sigma$, where ${\hat {\sigma }}$ is the sample standard deviation.

The assumptions underlying a t-test are that

• Z follows a standard normal distribution under the null hypothesis
• ps² follows a Χ² distribution with p degrees of freedom under the null hypothesis, where p is a positive constant
• Z and s are independent

In a specific type of t-test, these conditions are consequences of the population being studied, and of the way in which the data are sampled. For example, in the t-test comparing the means of two independent samples, the following assumptions should be met:

• Each of the two populations being compared should follow a normal distribution (which can be tested using a normality test, such as the Shapiro-Wilk and Kolmogorov-Smirnov tests, or which can be assessed graphically using a normal quantile plot).
• If using Student's original definition of the t-test, the two populations being compared should have the same variance (testable using Levene's test, Bartlett's test, or the Brown-Forsythe test; or assessable graphically using a normal quantile plot). If the sample sizes in the two groups being compared are roughly equal, Student's original t-test is highly robust to the presence of unequal variances . Welch's t-test is insensitive to equality of the variances regardless of whether the sample sizes are similar.
• The data used to carry out the test should be sampled independently from the two populations being compared. This is in general not testable from the data, but if the data are known to be dependently sampled (i.e. if they were sampled in clusters), then the classical t-tests discussed here may give misleading results.

Unpaired and paired two-sample t-tests

Two sample t-tests for a difference in mean can be either unpaired or paired. The unpaired, or "independent samples" t-test is used when two separate independent and identically distributed samples are obtained, one from each of the two populations being compared. For example, suppose we are evaluating the effect of a medical treatment, and we enroll 100 eligible subjects into our study, then randomize 50 subjects to the treatment group and 50 subjects to the control group. In this case, we have two independent samples and would use the unpaired form of the t-test. The randomization is not essential here — if we contacted 100 people by phone and obtained each person's age and gender, and then used a two-sample t-test to see whether the mean ages differ by gender, this would also be an independent samples t-test, even though the data are observational.

Dependent samples (or "paired") t-tests typically consist of a sample of matched pairs of similar units, or one group of units that has been tested twice (a "repeated measures" t-test). A typical example of the repeated measures t-test would be where subjects are tested prior to a treatment, say for high blood pressure, and the same subjects are tested again after treatment with a blood-pressure lowering medication.

A dependent t-test based on a "matched-pairs sample" results from an unpaired sample that is subsequently used to form a paired sample, by using additional variables that were measured along with the variable of interest . The matching is carried out by identifying pairs of values consisting of one observation from each of the two samples, where the pair is similar in terms of other measured variables. This approach is often used in observational studies to reduce or eliminate the effects of confounding factors. Suppose students in a particular school are given the opportunity to receive after-school mathematics tutoring. If only a fraction of the students complete the tutoring program, one might wish to evaluate the effectiveness of the program by comparing the students who did and who did not complete the program, using scores on a standardized test given after the program is finished. A difficulty is that the students who completed the tutoring program may already have differed in mathematical achievement before the tutoring program began. To reduce the confounding effect of baseline mathematical achievement, one can attempt to match each subject who completed the tutoring program to a subject who did not, matching on the students' mathematics grades from the previous semester. If we then compare the students within matched pairs using a paired t-test, baseline mathematical knowledge should have little effect on the results.

Note that a paired data set can always be analyzed using the unpaired or paired versions of the t-test, but an unpaired dataset must be analyzed using the unpaired t-test unless some form of pairing can be defined. An ideal pairing takes the form of blocking. For example, when comparing pre-treatment and post-treatment blood pressure within individuals, characteristics such as age and gender which are unrelated to the treatment but that may affect blood pressure do not affect the results of the paired t-test. In this case, the paired t-test will have greater power than the unpaired test. A different situation arises when following the matched-pairs strategy, where the goal is to reduce confounding. The cost of matching to reduce confounding is usually a reduction in power.

Calculations

Explicit expressions that can be used to carry out various t-tests are given below. In each case, the formula for a test statistic that either exactly follows or closely approximates a t-distribution under the null hypothesis is given. Also, the appropriate degrees of freedom is given in each case. Each of these statistics can be used to carry out either a one-tailed test or a two-tailed test.

Once a t value is determined, a p-value can be found using a table of values from Student's t-distribution. If the calculated p-value is below the threshold chosen for statistical significance (usually the 0.10, the 0.05, or 0.01 level), then the null hypothesis is rejected in favor of the alternative hypothesis.

Independent one-sample t-test

In testing the null hypothesis that the population mean is equal to a specified value μ₀, one uses the statistic

$t={\frac {{\overline {x}}-\mu _{0}}{s/{\sqrt {n}}}},$

where s is the sample standard deviation of the sample and n is the sample size. The degrees of freedom used in this test is n − 1.

Slope of a regression line

Suppose one is fitting the model

$Y_{i}=\alpha +\beta x_{i}+\varepsilon _{i},$

where x_i, i = 1, ..., n are known, α and β are unknown, and ε_i are independent normally distributed random errors with expected value 0 and unknown variance σ², and Y_i, i = 1, ..., n are observed. It is desired to test the null hypothesis that the slope β is equal to some specified value β₀ (often taken to be 0, in which case the hypothesis is that x and y are unrelated).

Let

${\begin{aligned}{\widehat {\alpha }},{\widehat {\beta }}&={\text{least-squares estimates}},\\SE_{\widehat {\alpha }},SE_{\widehat {\beta }}&={\text{the standard errors of least-squares estimates}}\end{aligned}}$

Then

$t_{score}={\frac {{\widehat {\beta }}-\beta _{0}}{SE_{\widehat {\beta }}}}$

has a t-distribution with n − 2 degrees of freedom if the null hypothesis is true. The standard error of the angular coefficient:

$SE_{\widehat {\beta }}={\frac {\sqrt {\frac {\sum _{i=1}^{n}(Y_{i}-{\widehat {y}}_{i})^{2}}{n-2}}}{\sqrt {\sum _{i=1}^{n}(x_{i}-{\overline {x}})^{2}}}}$

can be written in terms of the residuals. Let

${\begin{aligned}{\widehat {\varepsilon }}_{i}&=Y_{i}-{\widehat {y}}_{i}=Y_{i}-({\widehat {\alpha }}+{\widehat {\beta }}x_{i})={\text{residuals}}={\text{estimated errors}},\\{\text{SSE}}&=\sum _{i=1}^{n}{\widehat {\varepsilon }}_{i}^{\;2}={\text{sum of squares of residuals}}.\end{aligned}}$

Then $t_{score}$ is given by:

$t_{score}={\frac {({\widehat {\beta }}-\beta _{0}){\sqrt {n-2}}}{\sqrt {{\text{SSE}}/\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{2}}}}$

Independent two-sample t-test

Equal sample sizes, equal variance

This test is only used when both:

• the two sample sizes (that is, the number, n, of participants of each group) are equal;
• it can be assumed that the two distributions have the same variance.

Violations of these assumptions are discussed below.

The t statistic to test whether the means are different can be calculated as follows:

$t={\frac {{\bar {X}}_{1}-{\bar {X}}_{2}}{S_{X_{1}X_{2}}\cdot {\sqrt {\frac {2}{n}}}}}\$

where

$\ S_{X_{1}X_{2}}={\sqrt {\frac {S_{X_{1}}^{2}+S_{X_{2}}^{2}}{2}}}.$

Here $S_{X_{1}X_{2}}$ is the grand standard deviation (or pooled standard deviation), 1 = group one, 2 = group two. The denominator of t is the standard error of the difference between two means.

For significance testing, the degrees of freedom for this test is 2n − 2 where n is the number of participants in each group.

Unequal sample sizes, equal variance

This test is used only when it can be assumed that the two distributions have the same variance. (When this assumption is violated, see below.) The t statistic to test whether the means are different can be calculated as follows:

$t={\frac {{\bar {X}}_{1}-{\bar {X}}_{2}}{S_{X_{1}X_{2}}\cdot {\sqrt {{\frac {1}{n_{1}}}+{\frac {1}{n_{2}}}}}}}$

where

$S_{X_{1}X_{2}}={\sqrt {\frac {(n_{1}-1)S_{X_{1}}^{2}+(n_{2}-1)S_{X_{2}}^{2}}{n_{1}+n_{2}-2}}}.$

Note that the formulae above are generalizations for the case where both samples have equal sizes (substitute n₁ and n₂ for n and you'll see).

$S_{X_{1}X_{2}}$ is an estimator of the common standard deviation of the two samples: it is defined in this way so that its square is an unbiased estimator of the common variance whether or not the population means are the same. In these formulae, n = number of participants, 1 = group one, 2 = group two. n − 1 is the number of degrees of freedom for either group, and the total sample size minus two (that is, n₁ + n₂ − 2) is the total number of degrees of freedom, which is used in significance testing.

Unequal sample sizes, unequal variance

This test is used only when the two sample sizes are unequal and the variance is assumed to be different. See also Welch's t-test. The t statistic to test whether the means are different can be calculated as follows:

$t={{\overline {X}}_{1}-{\overline {X}}_{2} \over s_{{\overline {X}}_{1}-{\overline {X}}_{2}}}$

where

$s_{{\overline {X}}_{1}-{\overline {X}}_{2}}={\sqrt {{s_{1}^{2} \over {n}_{1}}+{s_{2}^{2} \over {n}_{2}}}}$

Where s² is the unbiased estimator of the variance of the two samples, n = number of participants, 1 = group one, 2 = group two. Note that in this case, $s_{{\overline {X}}_{1}-{\overline {X}}_{2}}^{2}$ is not a pooled variance. For use in significance testing, the distribution of the test statistic is approximated as being an ordinary Student's t distribution with the degrees of freedom calculated using

$\mathrm {D.F.} ={\frac {(s_{1}^{2}/n_{1}+s_{2}^{2}/n_{2})^{2}}{(s_{1}^{2}/n_{1})^{2}/(n_{1}-1)+(s_{2}^{2}/n_{2})^{2}/(n_{2}-1)}}.$

This is called the Welch-Satterthwaite equation. Note that the true distribution of the test statistic actually depends (slightly) on the two unknown variances: see Behrens–Fisher problem.

Dependent t-test for paired samples

This test is used when the samples are dependent; that is, when there is only one sample that has been tested twice (repeated measures) or when there are two samples that have been matched or "paired". This is an example of a paired difference test.

$t={\frac {{\overline {X}}_{D}-\mu _{0}}{{s_{D}}/{\sqrt {N}}}}.$

For this equation, the differences between all pairs must be calculated. The pairs are either one person's pre-test and post-test scores or between pairs of persons matched into meaningful groups (for instance drawn from the same family or age group: see table). The average (X_D) and standard deviation (s_D) of those differences are used in the equation. The constant μ₀ is non-zero if you want to test whether the average of the difference is significantly different than μ₀. The degree of freedom used is N − 1.

Example of repeated measures
Number	Name	Test 1	Test 2
1	Mike	35%	67%
2	Melanie	50%	46%
3	Melissa	90%	86%
4	Mitchell	78%	91%

Example of matched pairs
Pair	Name	Age	Test
1	Jon	35	250
1	Jane	36	340
2	Jimmy	22	460
2	Jessy	21	200

Worked examples

Suppose two random samples of screws have weights

30.02, 29.99, 30.11, 29.97, 30.01, 29.99

and

29.89, 29.93, 29.72, 29.98, 30.02, 29.98.

We will carry out tests of the null hypothesis that the population means underlying the two samples are equal.

The difference between the two sample averages, which appears in the numerator for all the two-sample testing approaches discussed above, is

${\overline {X}}_{1}-{\overline {X}}_{2}=0.095.$

The sample standard deviations for the two samples are approximately 0.05 and 0.11, respectively. For such small samples, a test of equality between the two population variances would not be very powerful. Since the sample sizes are equal, the two forms of the two sample t-test will perform similarly in this example.

Unequal variances

If we follow the approach for unequal variances, discussed above, we get

${\sqrt {{s_{1}^{2} \over {n}_{1}}+{s_{2}^{2} \over {n}_{2}}}}\approx 0.0485$

and

${\rm {df}}\approx 7.03.$

The test statistic is approximately 1.959. The two-tailed p-value is approximately 0.091 and the one-tailed p-value is approximately 0.045.

Equal variances

If we follow the approach for equal variance, discussed above, we get

$S_{X_{1}X_{2}}\approx 0.084$

and

$df=10.$

Since the sample sizes are equal (both are 6), the test statistic is again approximately equal to 1.959. Since the degrees of freedom is different form the unequal variances test, the p-values will differ slightly from what was found above. Here, the two-tailed p-value is approximately 0.078, and the one-tailed p-value is approximately 0.039. Thus if we have good reason to believe that the population variances are equal, the results become somewhat more suggestive of a difference in the mean weights for the two populations of screws.

Alternatives to the t-test for location problems

The t-test provides an exact test for the equality of the means of two normal populations with unknown, but equal, variances. The Welch's t-test is a nearly-exact test for the case where the data are normal but the variances may differ. For moderately large samples, the t-test becomes very similar to the Z-test, and is therefore robust to moderate violations of the normality assumption for moderate or large sample sizes.

The t-test and Z-test require normality of the sample means. The t-test requires in addition that the sample variance follows a scaled Χ² distribution, and that the sample mean and sample variance be statistically independent. Normality of the individual data values is not required if these conditions are met. By the central limit theorem, sample means of moderately large samples are often well-approximated by a normal distribution even if the data are not normally distributed. For non-normal data, the distribution of the sample variance may deviate substantially from a χ² distribution. However, if the sample size is large Slutsky's theorem implies that the distribution of the sample variance has little effect on the distribution of the test statistic. If the data are substantially non-normal and the sample size is small, the t-test can give misleading results.

To relax the normality assumption, a non-parametric alternative to the t-test can be used, at a cost of lower statistical power. The usual choices for non-parametric location tests are the Mann-Whitney U test for independent samples, and the binomial test or the Wilcoxon signed-rank test for paired samples.

One-way analysis of variance generalizes the two-sample t-test when the data belong to more than two groups.

Multivariate testing

A generalization of Student's t statistic, called Hotelling's T-square statistic, allows for the testing of hypotheses on multiple (often correlated) measures within the same sample. For instance, a researcher might submit a number of subjects to a personality test consisting of multiple personality scales (e.g. the Big Five). Because measures of this type are usually highly correlated, it is not advisable to conduct separate univariate t-tests to test hypotheses, as these would neglect the covariance among measures and inflate the chance of falsely rejecting at least one hypothesis (Type I error). In this case a single multivariate test is preferable for hypothesis testing. Hotelling's T ² statistic follows a T ² distribution. However, in practice the distribution is rarely used, and instead converted to an F distribution.

One-sample T ² test

For a one-sample multivariate test, the hypothesis is that the mean vector (${\mathbf {\mu } }$) is equal to a given vector (${\mathbf {\mu } _{0}}$). The test statistic is defined as:

$T^{2}=n({\overline {\mathbf {x} }}-{\mathbf {\mu } _{0}})'{\mathbf {S} }^{-1}({\overline {\mathbf {x} }}-{\mathbf {\mu } _{0}})$

where $n$ is the sample size, ${\overline {\mathbf {x} }}$ is the vector of column means and ${\mathbf {S} }$ is a $m\times m$ sample covariance matrix.

Two-sample T ² test

For a two-sample multivariate test, the hypothesis is that the mean vectors (${\mathbf {\mu } _{1}}$, ${\mathbf {\mu } _{2}}$) of two samples are equal. The test statistic is defined as

$T^{2}={\frac {n_{1}n_{2}}{n_{1}+n_{2}}}({\overline {\mathbf {x} _{1}}}-{\overline {\mathbf {x} _{2}}})'{\mathbf {S} _{\text{pooled}}}^{-1}({\overline {\mathbf {x} _{1}}}-{\overline {\mathbf {x} _{2}}}).$

Implementations

Most spreadsheet programs and statistics packages, such as Microsoft Excel, SPSS, DAP, gretl, GNU/R and PSPP include implementations of Student's t-test.

See also

• Student's t-statistic
• F-test

References

• O'Mahony, Michael (1986). Sensory Evaluation of Food: Statistical Methods and Procedures. CRC Press. p. 487. ISBN 0-824-77337-3.

• Press, William H. (1992). Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press. pp. p. 616. ISBN 0-521-43108-5. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)

External links

• A conceptual article on the Student's t-test

Online calculators

• Online T-Test Calculator
• 2 Sample T-Test Calculator
• GraphPad's Paired/Unpaired/Welch T-Test Calculator
• OpenEpi software program