Boost C++ Libraries

...one of the most highly regarded and expertly designed C++ library projects in the world. Herb Sutter and Andrei Alexandrescu, C++ Coding Standards

This is the documentation for a snapshot of the develop branch, built from commit eb47817f5a.
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t-tests

Synopsis

#include <boost/math/statistics/t_test.hpp>

namespace boost::math::statistics {

template<typename Real, typename Size = Real>
std::pair<Real, Real> one_sample_t_test(Real sample_mean, Real sample_variance, Size num_samples, Real assumed_mean);

template<typename ForwardIterator, typename Real = typename std::iterator_traits<ForwardIterator>::value_type>
std::pair<Real, Real> one_sample_t_test(ForwardIterator begin, ForwardIterator end, Real assumed_mean);

template<typename Container>
std::pair<Real, Real> one_sample_t_test(Container const & v, typename Container::value_type assumed_mean);

template<typename ForwardIterator, typename Real = typename std::iterator_traits<ForwardIterator>::value_type>
std::pair<Real, Real> two_sample_t_test(ForwardIterator begin_1, ForwardIterator end_1, ForwardIterator begin_2, ForwardIterator begin_2);

template<typename Container, typename Real = typename Container::value_type>
std::pair<Real, Real> two_sample_t_test(Container const & u, Container const & v);

template<typename ForwardIterator, typename Real = typename std::iterator_traits<ForwardIterator>::value_type>
std::pair<Real, Real> paired_samples_t_test(ForwardIterator begin_1, ForwardIterator end_1, ForwardIterator begin_2, ForwardIterator begin_2);

template<typename Container, typename Real = typename Container::value_type>
std::pair<Real, Real> paired_samples_t_test(Container const & u, Container const & v);

}

Background

A set of C++11 compatible functions for various Student's t-tests. The input can be any real number or set of real numbers. In the event that the input is an integer or a set of integers typename Real will be deduced as a double precision type.

One-sample t-test

A one-sample t-test attempts to answer the question "given a sample mean, is it likely that the population mean of my data is a certain value?" The test statistic is

where µ0 is the assumed mean, s2 is the sample variance, and n is the number of samples. If the absolute value of the test statistic is large, then we have low confidence that the population mean is equal to µ0, and if the absolute value of the test statistic is small, we have high confidence. We now ask the question "what constitutes large and small in this context?"

Under reasonable assumptions, the test statistic t can be assumed to come from a Student's t-distribution. Since we wish to know if the sample mean deviates from the true mean in either direction, the test is two-tailed. Hence the p-value is straightforward to calculate from the Student's t-distribution on n - 1 degrees of freedom, but nonetheless it is convenient to have it computed here.

An example usage is as follows:

#include <vector>
#include <random>
#include <boost/math/statistics/t_test.hpp>

std::random_device rd;
std::mt19937 gen{rd()};
std::normal_distribution<double> dis{0,1};
std::vector<double> v(1024);
for (auto & x : v) {
  x = dis(gen);
}

auto [t, p] = boost::math::statistics::one_sample_t_test(v, 0.0);

The test statistic is the first element of the pair, and the p-value is the second element.

Independent two-sample t-test

A two-sample t-test determines if the means of two sets of data have a statistically significant difference from each other. There are two underlying implementations based off the variances of the sets of data. If s1 and s2 are within a factor of 2 of each other the following formulas will be used:

where

If s1 and s2 are not within a factor of 2 of each other Welch's t-test will be used:

where

and

An example of usage is as follows:

#include <vector>
#include <random>
#include <boost/math/statistics/t_test.hpp>

std::random_device rd;
std::mt19937 gen{rd()};
std::normal_distribution<double> dis{0,1};
std::vector<double> u(1024);
std::vector<double> v(1024);
for(std::size_t i = 0; i < u.size(); ++i)
{
  u[i] = dis(gen);
  v[i] = dis(gen);
}

auto [t, p] = boost::math::statistics::two_sample_t_test(u, v);

Nota bene: The sample sizes for the two sets of data do not need to be equal.

Nota bene: std::distance is used in the implementation leading to lower performance with ForwardIterator than RandomAccessIterator.

Dependent t-test for paired samples

This test is used when the samples are dependent such as when one sample has been tested twice, or when two samples have been paired. Much like the independent t-test it is used to determine if the means of two sets of data have a statistically significant difference from each other.

where XD and sD are the mean and standard deviation of the differences between all pairs.

An example of usage is as follows:

#include <vector>
#include <random>
#include <boost/math/statistics/t_test.hpp>

std::vector<int> first_test {35, 50, 90, 78};
std::vector<int> second_test {67, 46, 86, 91};

auto [t, p] = boost::math::statistics::paired_samples_t_test(first_test, second_test);

Performance

There are two cases: Where the mean and sample variance have already been computed, and the case where the mean and sample variance must be computed on the fly.

----------------------------------------------
Benchmark                                Time
----------------------------------------------
OneSampleTTest<double>/8               291 ns bytes_per_second=210.058M/s
OneSampleTTest<double>/16             1064 ns bytes_per_second=114.697M/s
OneSampleTTest<double>/32              407 ns bytes_per_second=599.213M/s
OneSampleTTest<double>/64              595 ns bytes_per_second=821.086M/s
OneSampleTTest<double>/128            1475 ns bytes_per_second=662.071M/s
OneSampleTTest<double>/256            1746 ns bytes_per_second=1118.85M/s
OneSampleTTest<double>/512            3303 ns bytes_per_second=1.15492G/s
OneSampleTTest<double>/1024           6404 ns bytes_per_second=1.19139G/s
OneSampleTTest<double>/2048          12461 ns bytes_per_second=1.2245G/s
OneSampleTTest<double>/4096          24805 ns bytes_per_second=1.23029G/s
OneSampleTTest<double>/8192          49639 ns bytes_per_second=1.22956G/s
OneSampleTTest<double>/16384         98685 ns bytes_per_second=1.23698G/s
OneSampleTTest<double>/32768        197434 ns bytes_per_second=1.23656G/s
OneSampleTTest<double>/65536        393929 ns bytes_per_second=1.23952G/s
OneSampleTTest<double>/131072       790967 ns bytes_per_second=1.23466G/s
OneSampleTTest<double>/262144      1582366 ns bytes_per_second=1.23434G/s
OneSampleTTest<double>/524288      3141112 ns bytes_per_second=1.24358G/s
OneSampleTTest<double>/1048576     6260407 ns bytes_per_second=1.24792G/s
OneSampleTTest<double>/2097152    12521811 ns bytes_per_second=1.24784G/s
OneSampleTTest<double>/4194304    25076257 ns bytes_per_second=1.24619G/s
OneSampleTTest<double>/8388608    50226183 ns bytes_per_second=1.2444G/s
OneSampleTTest<double>/16777216  100522789 ns bytes_per_second=1.24353G/s
OneSampleTTest<double>_BigO           5.99 N
OneSampleTTest<double>_RMS               0 %
OneSampleTTestKnownMeanAndVariance<double>        207 ns

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