One of the most frequent statistical problems is testing hypotheses about the mean of the samples considered.

This test is used to check hypotheses about the fact that the mean of random variable *X* equals to given μ. Testing sample should be a sample of a normal random variable.
During its work, the test calculates t-statistic:

If X has a normal distribution, the t-statistic will have Student's distribution with N-1 degrees of freedom. This allows the use of the Student's distribution to define the significance level which corresponds to the value of t-statistic.

**Note #1**

If *X* is not normal, *t* will have an unknown distribution and, strictly speaking, the t-test is inapplicable. However, according to the central limit theorem, as the sample size increases, the distribution of *t* tends to be normal. Therefore, if the sample size is big, we can use the t-test even if *X* is not normal. But there is no way to find out what value is big enough. This value depends on how *X* deviates from the normal distribution. Some sources claim that *N* should be greater than 30, but sometimes even this size is not enough. Alternatively, we can use non-parametric test: sign test or Wilcoxon rank-sign test.

Subroutine **StudentTTest1** returns three p-values:

- p-value for two-tailed test (null hypothesis - mean is equal to the given number)
- p-value for left-tailed test (null hypothesis - mean is greater than or equal to the given number)
- p-value for right-tailed test (null hypothesis - mean is less than or equal to the given number)

This test checks hypotheses about the fact that the means of two random variables *X* and *Y* which are represented by samples *x _{S }* and

- both random variables have a normal distribution
- dispersions are equal (or slightly different)
- samples are independent.

During its work, the test calculates t-statistic:

If *X* and *Y* have a normal distribution, the t-statistic will have Student's distribution with *N _{X }+N_{Y }-2* degrees of freedom. This allows the use of the Student's distribution to define a significance level which corresponds to the value of t-statistic.

**Note #2**

If *X* or *Y* is not normal, *t* will have an unknown distribution and, strictly speaking, the t-test is inapplicable. However, according to the central limit theorem, as the sample sizes increase, the distribution of *t* tends to be normal. Therefore, if sample sizes are big enough, we can use the t-test even if *X* or *Y* is not normal. But there is no way to find what values for *N _{X }* and

Subroutine **StudentTTest2** returns three p-values:

- p-value for two-tailed test (null hypothesis - means are equal)
- p-value for left-tailed test (null hypothesis - mean of the first sample is greater than or equal to the mean of the second sample)
- p-value for right-tailed test (null hypothesis - mean of the first sample is less than or equal to the mean of the second sample)

This test checks hypotheses about the fact that the means of two random variables *X* and *Y* which are represented by samples *x _{S }* and

- both random variables have a normal distribution
- samples are independent.

Dispersion equality is not required.

During its work, the test calculates the t-statistic:

If *X* and *Y* have a normal distribution, the t-statistic will have Student's distribution with *DF* degrees of freedom:

This allows the use of the Student's distribution to define the significance level which corresponds to the value of the t-statistic.

**Note #3**

If *X* or *Y* is not normal, *t* will have an unknown distribution and, strictly speaking, the t-test is inapplicable. However, according to the central limit theorem, as the sample sizes increase, the distribution of *t* tends to be normal. Therefore, if sample sizes are big enough, we can use the t-test even if *X* or *Y* is not normal. But there is no way to find what values for *N _{X }* and

Subroutine **UnequalVarianceTTest** returns three p-values:

- p-value for two-tailed test (null hypothesis - means are equal)
- p-value for left-tailed test (null hypothesis - mean of the first sample is greater than or equal to the mean of the second sample)
- p-value for right-tailed test (null hypothesis - mean of the first sample is less than or equal to the mean of the second sample).

C++ | `studentttests` subpackage | |

C# | `studentttests` subpackage |

*This article is intended for personal use only.*

C# source.

C++ source.

C++ source. MPFR/GMP is used.

GMP source is available from gmplib.org. MPFR source is available from www.mpfr.org.

FreePascal version.

Delphi version.

VB.NET version.

VBA version.

Python version (CPython and IronPython are supported).

ALGLIB® - numerical analysis library, 1999-2017.

ALGLIB is a registered trademark of the ALGLIB Project.

Policies for this site: privacy policy, trademark policy.

ALGLIB is a registered trademark of the ALGLIB Project.

Policies for this site: privacy policy, trademark policy.