TTest
org.apache.commons.math3.stat.inference

## Class TTest

• `public class TTestextends Object`
An implementation for Student's t-tests.

Tests can be:

• One-sample or two-sample
• One-sided or two-sided
• Paired or unpaired (for two-sample tests)
• Homoscedastic (equal variance assumption) or heteroscedastic (for two sample tests)
• Fixed significance level (boolean-valued) or returning p-values.

Test statistics are available for all tests. Methods including "Test" in in their names perform tests, all other methods return t-statistics. Among the "Test" methods, `double-`valued methods return p-values; `boolean-`valued methods perform fixed significance level tests. Significance levels are always specified as numbers between 0 and 0.5 (e.g. tests at the 95% level use `alpha=0.05`).

Input to tests can be either `double[]` arrays or `StatisticalSummary` instances.

Uses commons-math `TDistribution` implementation to estimate exact p-values.

• ### Constructor Summary

Constructors
Constructor and Description
`TTest()`
• ### Method Summary

Methods
Modifier and TypeMethod and Description
`double``homoscedasticT(double[] sample1, double[] sample2)`
Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
`double``homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
Computes a 2-sample t statistic, comparing the means of the datasets described by two `StatisticalSummary` instances, under the assumption of equal subpopulation variances.
`double``homoscedasticTTest(double[] sample1, double[] sample2)`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
`boolean``homoscedasticTTest(double[] sample1, double[] sample2, double alpha)`
Performs a two-sided t-test evaluating the null hypothesis that `sample1` and `sample2` are drawn from populations with the same mean, with significance level `alpha`, assuming that the subpopulation variances are equal.
`double``homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
`double``pairedT(double[] sample1, double[] sample2)`
Computes a paired, 2-sample t-statistic based on the data in the input arrays.
`double``pairedTTest(double[] sample1, double[] sample2)`
Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
`boolean``pairedTTest(double[] sample1, double[] sample2, double alpha)`
Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between `sample1` and `sample2` is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level `alpha`.
`double``t(double[] sample1, double[] sample2)`
Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
`double``t(double mu, double[] observed)`
Computes a t statistic given observed values and a comparison constant.
`double``t(double mu, StatisticalSummary sampleStats)`
Computes a t statistic to use in comparing the mean of the dataset described by `sampleStats` to `mu`.
`double``t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
Computes a 2-sample t statistic , comparing the means of the datasets described by two `StatisticalSummary` instances, without the assumption of equal subpopulation variances.
`double``tTest(double[] sample1, double[] sample2)`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
`boolean``tTest(double[] sample1, double[] sample2, double alpha)`
Performs a two-sided t-test evaluating the null hypothesis that `sample1` and `sample2` are drawn from populations with the same mean, with significance level `alpha`.
`double``tTest(double mu, double[] sample)`
Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant `mu`.
`boolean``tTest(double mu, double[] sample, double alpha)`
Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which `sample` is drawn equals `mu`.
`double``tTest(double mu, StatisticalSummary sampleStats)`
Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by `sampleStats` with the constant `mu`.
`boolean``tTest(double mu, StatisticalSummary sampleStats, double alpha)`
Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by `stats` is drawn equals `mu`.
`double``tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
`boolean``tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)`
Performs a two-sided t-test evaluating the null hypothesis that `sampleStats1` and `sampleStats2` describe datasets drawn from populations with the same mean, with significance level `alpha`.
• ### Methods inherited from class java.lang.Object

`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Constructor Detail

• #### TTest

`public TTest()`
• ### Method Detail

• #### pairedT

`public double pairedT(double[] sample1,             double[] sample2)               throws NullArgumentException,                      NoDataException,                      DimensionMismatchException,                      NumberIsTooSmallException`
Computes a paired, 2-sample t-statistic based on the data in the input arrays. The t-statistic returned is equivalent to what would be returned by computing the one-sample t-statistic `t(double, double[])`, with `mu = 0` and the sample array consisting of the (signed) differences between corresponding entries in `sample1` and `sample2.`

Preconditions:

• The input arrays must have the same length and their common length must be at least 2.

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
Returns:
t statistic
Throws:
`NullArgumentException` - if the arrays are `null`
`NoDataException` - if the arrays are empty
`DimensionMismatchException` - if the length of the arrays is not equal
`NumberIsTooSmallException` - if the length of the arrays is < 2
• #### pairedTTest

`public double pairedTTest(double[] sample1,                 double[] sample2)                   throws NullArgumentException,                          NoDataException,                          DimensionMismatchException,                          NumberIsTooSmallException,                          MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.

The number returned is the smallest significance level at which one can reject the null hypothesis that the mean of the paired differences is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0. For a one-sided test, divide the returned value by 2.

This test is equivalent to a one-sample t-test computed using `tTest(double, double[])` with `mu = 0` and the sample array consisting of the signed differences between corresponding elements of `sample1` and `sample2.`

Usage Note:
The validity of the p-value depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The input array lengths must be the same and their common length must be at least 2.

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
Returns:
p-value for t-test
Throws:
`NullArgumentException` - if the arrays are `null`
`NoDataException` - if the arrays are empty
`DimensionMismatchException` - if the length of the arrays is not equal
`NumberIsTooSmallException` - if the length of the arrays is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### pairedTTest

`public boolean pairedTTest(double[] sample1,                  double[] sample2,                  double alpha)                    throws NullArgumentException,                           NoDataException,                           DimensionMismatchException,                           NumberIsTooSmallException,                           OutOfRangeException,                           MaxCountExceededException`
Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between `sample1` and `sample2` is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level `alpha`.

Returns `true` iff the null hypothesis can be rejected with confidence `1 - alpha`. To perform a 1-sided test, use `alpha * 2`

Usage Note:
The validity of the test depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The input array lengths must be the same and their common length must be at least 2.
• ` 0 < alpha < 0.5 `

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
`alpha` - significance level of the test
Returns:
true if the null hypothesis can be rejected with confidence 1 - alpha
Throws:
`NullArgumentException` - if the arrays are `null`
`NoDataException` - if the arrays are empty
`DimensionMismatchException` - if the length of the arrays is not equal
`NumberIsTooSmallException` - if the length of the arrays is < 2
`OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
`MaxCountExceededException` - if an error occurs computing the p-value
• #### t

`public double t(double mu,       double[] observed)         throws NullArgumentException,                NumberIsTooSmallException`
Computes a t statistic given observed values and a comparison constant.

This statistic can be used to perform a one sample t-test for the mean.

Preconditions:

• The observed array length must be at least 2.

Parameters:
`mu` - comparison constant
`observed` - array of values
Returns:
t statistic
Throws:
`NullArgumentException` - if `observed` is `null`
`NumberIsTooSmallException` - if the length of `observed` is < 2
• #### t

`public double t(double mu,       StatisticalSummary sampleStats)         throws NullArgumentException,                NumberIsTooSmallException`
Computes a t statistic to use in comparing the mean of the dataset described by `sampleStats` to `mu`.

This statistic can be used to perform a one sample t-test for the mean.

Preconditions:

• `observed.getN() ≥ 2`.

Parameters:
`mu` - comparison constant
`sampleStats` - DescriptiveStatistics holding sample summary statitstics
Returns:
t statistic
Throws:
`NullArgumentException` - if `sampleStats` is `null`
`NumberIsTooSmallException` - if the number of samples is < 2
• #### homoscedasticT

`public double homoscedasticT(double[] sample1,                    double[] sample2)                      throws NullArgumentException,                             NumberIsTooSmallException`
Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances. To compute a t-statistic without the equal variances hypothesis, use `t(double[], double[])`.

This statistic can be used to perform a (homoscedastic) two-sample t-test to compare sample means.

The t-statistic is

` t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))`

where `n1` is the size of first sample; ` n2` is the size of second sample; ` m1` is the mean of first sample; ` m2` is the mean of second sample

and `var` is the pooled variance estimate:

`var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))`

with `var1` the variance of the first sample and `var2` the variance of the second sample.

Preconditions:

• The observed array lengths must both be at least 2.

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
Returns:
t statistic
Throws:
`NullArgumentException` - if the arrays are `null`
`NumberIsTooSmallException` - if the length of the arrays is < 2
• #### t

`public double t(double[] sample1,       double[] sample2)         throws NullArgumentException,                NumberIsTooSmallException`
Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances. To compute a t-statistic assuming equal variances, use `homoscedasticT(double[], double[])`.

This statistic can be used to perform a two-sample t-test to compare sample means.

The t-statistic is

` t = (m1 - m2) / sqrt(var1/n1 + var2/n2)`

where `n1` is the size of the first sample ` n2` is the size of the second sample; ` m1` is the mean of the first sample; ` m2` is the mean of the second sample; ` var1` is the variance of the first sample; ` var2` is the variance of the second sample;

Preconditions:

• The observed array lengths must both be at least 2.

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
Returns:
t statistic
Throws:
`NullArgumentException` - if the arrays are `null`
`NumberIsTooSmallException` - if the length of the arrays is < 2
• #### t

`public double t(StatisticalSummary sampleStats1,       StatisticalSummary sampleStats2)         throws NullArgumentException,                NumberIsTooSmallException`
Computes a 2-sample t statistic , comparing the means of the datasets described by two `StatisticalSummary` instances, without the assumption of equal subpopulation variances. Use `homoscedasticT(StatisticalSummary, StatisticalSummary)` to compute a t-statistic under the equal variances assumption.

This statistic can be used to perform a two-sample t-test to compare sample means.

The returned t-statistic is

` t = (m1 - m2) / sqrt(var1/n1 + var2/n2)`

where `n1` is the size of the first sample; ` n2` is the size of the second sample; ` m1` is the mean of the first sample; ` m2` is the mean of the second sample ` var1` is the variance of the first sample; ` var2` is the variance of the second sample

Preconditions:

• The datasets described by the two Univariates must each contain at least 2 observations.

Parameters:
`sampleStats1` - StatisticalSummary describing data from the first sample
`sampleStats2` - StatisticalSummary describing data from the second sample
Returns:
t statistic
Throws:
`NullArgumentException` - if the sample statistics are `null`
`NumberIsTooSmallException` - if the number of samples is < 2
• #### homoscedasticT

`public double homoscedasticT(StatisticalSummary sampleStats1,                    StatisticalSummary sampleStats2)                      throws NullArgumentException,                             NumberIsTooSmallException`
Computes a 2-sample t statistic, comparing the means of the datasets described by two `StatisticalSummary` instances, under the assumption of equal subpopulation variances. To compute a t-statistic without the equal variances assumption, use `t(StatisticalSummary, StatisticalSummary)`.

This statistic can be used to perform a (homoscedastic) two-sample t-test to compare sample means.

The t-statistic returned is

` t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))`

where `n1` is the size of first sample; ` n2` is the size of second sample; ` m1` is the mean of first sample; ` m2` is the mean of second sample and `var` is the pooled variance estimate:

`var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))`

with `var1` the variance of the first sample and `var2` the variance of the second sample.

Preconditions:

• The datasets described by the two Univariates must each contain at least 2 observations.

Parameters:
`sampleStats1` - StatisticalSummary describing data from the first sample
`sampleStats2` - StatisticalSummary describing data from the second sample
Returns:
t statistic
Throws:
`NullArgumentException` - if the sample statistics are `null`
`NumberIsTooSmallException` - if the number of samples is < 2
• #### tTest

`public double tTest(double mu,           double[] sample)             throws NullArgumentException,                    NumberIsTooSmallException,                    MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant `mu`.

The number returned is the smallest significance level at which one can reject the null hypothesis that the mean equals `mu` in favor of the two-sided alternative that the mean is different from `mu`. For a one-sided test, divide the returned value by 2.

Usage Note:
The validity of the test depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The observed array length must be at least 2.

Parameters:
`mu` - constant value to compare sample mean against
`sample` - array of sample data values
Returns:
p-value
Throws:
`NullArgumentException` - if the sample array is `null`
`NumberIsTooSmallException` - if the length of the array is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### tTest

`public boolean tTest(double mu,            double[] sample,            double alpha)              throws NullArgumentException,                     NumberIsTooSmallException,                     OutOfRangeException,                     MaxCountExceededException`
Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which `sample` is drawn equals `mu`.

Returns `true` iff the null hypothesis can be rejected with confidence `1 - alpha`. To perform a 1-sided test, use `alpha * 2`

Examples:

1. To test the (2-sided) hypothesis `sample mean = mu ` at the 95% level, use
`tTest(mu, sample, 0.05) `
2. To test the (one-sided) hypothesis ` sample mean < mu ` at the 99% level, first verify that the measured sample mean is less than `mu` and then use
`tTest(mu, sample, 0.02) `

Usage Note:
The validity of the test depends on the assumptions of the one-sample parametric t-test procedure, as discussed here

Preconditions:

• The observed array length must be at least 2.

Parameters:
`mu` - constant value to compare sample mean against
`sample` - array of sample data values
`alpha` - significance level of the test
Returns:
p-value
Throws:
`NullArgumentException` - if the sample array is `null`
`NumberIsTooSmallException` - if the length of the array is < 2
`OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
`MaxCountExceededException` - if an error computing the p-value
• #### tTest

`public double tTest(double mu,           StatisticalSummary sampleStats)             throws NullArgumentException,                    NumberIsTooSmallException,                    MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by `sampleStats` with the constant `mu`.

The number returned is the smallest significance level at which one can reject the null hypothesis that the mean equals `mu` in favor of the two-sided alternative that the mean is different from `mu`. For a one-sided test, divide the returned value by 2.

Usage Note:
The validity of the test depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The sample must contain at least 2 observations.

Parameters:
`mu` - constant value to compare sample mean against
`sampleStats` - StatisticalSummary describing sample data
Returns:
p-value
Throws:
`NullArgumentException` - if `sampleStats` is `null`
`NumberIsTooSmallException` - if the number of samples is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### tTest

`public boolean tTest(double mu,            StatisticalSummary sampleStats,            double alpha)              throws NullArgumentException,                     NumberIsTooSmallException,                     OutOfRangeException,                     MaxCountExceededException`
Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by `stats` is drawn equals `mu`.

Returns `true` iff the null hypothesis can be rejected with confidence `1 - alpha`. To perform a 1-sided test, use `alpha * 2.`

Examples:

1. To test the (2-sided) hypothesis `sample mean = mu ` at the 95% level, use
`tTest(mu, sampleStats, 0.05) `
2. To test the (one-sided) hypothesis ` sample mean < mu ` at the 99% level, first verify that the measured sample mean is less than `mu` and then use
`tTest(mu, sampleStats, 0.02) `

Usage Note:
The validity of the test depends on the assumptions of the one-sample parametric t-test procedure, as discussed here

Preconditions:

• The sample must include at least 2 observations.

Parameters:
`mu` - constant value to compare sample mean against
`sampleStats` - StatisticalSummary describing sample data values
`alpha` - significance level of the test
Returns:
p-value
Throws:
`NullArgumentException` - if `sampleStats` is `null`
`NumberIsTooSmallException` - if the number of samples is < 2
`OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
`MaxCountExceededException` - if an error occurs computing the p-value
• #### tTest

`public double tTest(double[] sample1,           double[] sample2)             throws NullArgumentException,                    NumberIsTooSmallException,                    MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.

The number returned is the smallest significance level at which one can reject the null hypothesis that the two means are equal in favor of the two-sided alternative that they are different. For a one-sided test, divide the returned value by 2.

The test does not assume that the underlying popuation variances are equal and it uses approximated degrees of freedom computed from the sample data to compute the p-value. The t-statistic used is as defined in `t(double[], double[])` and the Welch-Satterthwaite approximation to the degrees of freedom is used, as described here. To perform the test under the assumption of equal subpopulation variances, use `homoscedasticTTest(double[], double[])`.

Usage Note:
The validity of the p-value depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The observed array lengths must both be at least 2.

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
Returns:
p-value for t-test
Throws:
`NullArgumentException` - if the arrays are `null`
`NumberIsTooSmallException` - if the length of the arrays is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### homoscedasticTTest

`public double homoscedasticTTest(double[] sample1,                        double[] sample2)                          throws NullArgumentException,                                 NumberIsTooSmallException,                                 MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances. To perform the test without the equal variances assumption, use `tTest(double[], double[])`.

The number returned is the smallest significance level at which one can reject the null hypothesis that the two means are equal in favor of the two-sided alternative that they are different. For a one-sided test, divide the returned value by 2.

A pooled variance estimate is used to compute the t-statistic. See `homoscedasticT(double[], double[])`. The sum of the sample sizes minus 2 is used as the degrees of freedom.

Usage Note:
The validity of the p-value depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The observed array lengths must both be at least 2.

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
Returns:
p-value for t-test
Throws:
`NullArgumentException` - if the arrays are `null`
`NumberIsTooSmallException` - if the length of the arrays is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### tTest

`public boolean tTest(double[] sample1,            double[] sample2,            double alpha)              throws NullArgumentException,                     NumberIsTooSmallException,                     OutOfRangeException,                     MaxCountExceededException`
Performs a two-sided t-test evaluating the null hypothesis that `sample1` and `sample2` are drawn from populations with the same mean, with significance level `alpha`. This test does not assume that the subpopulation variances are equal. To perform the test assuming equal variances, use `homoscedasticTTest(double[], double[], double)`.

Returns `true` iff the null hypothesis that the means are equal can be rejected with confidence `1 - alpha`. To perform a 1-sided test, use `alpha * 2`

See `t(double[], double[])` for the formula used to compute the t-statistic. Degrees of freedom are approximated using the Welch-Satterthwaite approximation.

Examples:

1. To test the (2-sided) hypothesis `mean 1 = mean 2 ` at the 95% level, use
`tTest(sample1, sample2, 0.05). `
2. To test the (one-sided) hypothesis ` mean 1 < mean 2 `, at the 99% level, first verify that the measured mean of `sample 1` is less than the mean of `sample 2` and then use
`tTest(sample1, sample2, 0.02) `

Usage Note:
The validity of the test depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The observed array lengths must both be at least 2.
• ` 0 < alpha < 0.5 `

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
`alpha` - significance level of the test
Returns:
true if the null hypothesis can be rejected with confidence 1 - alpha
Throws:
`NullArgumentException` - if the arrays are `null`
`NumberIsTooSmallException` - if the length of the arrays is < 2
`OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
`MaxCountExceededException` - if an error occurs computing the p-value
• #### homoscedasticTTest

`public boolean homoscedasticTTest(double[] sample1,                         double[] sample2,                         double alpha)                           throws NullArgumentException,                                  NumberIsTooSmallException,                                  OutOfRangeException,                                  MaxCountExceededException`
Performs a two-sided t-test evaluating the null hypothesis that `sample1` and `sample2` are drawn from populations with the same mean, with significance level `alpha`, assuming that the subpopulation variances are equal. Use `tTest(double[], double[], double)` to perform the test without the assumption of equal variances.

Returns `true` iff the null hypothesis that the means are equal can be rejected with confidence `1 - alpha`. To perform a 1-sided test, use `alpha * 2.` To perform the test without the assumption of equal subpopulation variances, use `tTest(double[], double[], double)`.

A pooled variance estimate is used to compute the t-statistic. See `t(double[], double[])` for the formula. The sum of the sample sizes minus 2 is used as the degrees of freedom.

Examples:

1. To test the (2-sided) hypothesis `mean 1 = mean 2 ` at the 95% level, use
`tTest(sample1, sample2, 0.05). `
2. To test the (one-sided) hypothesis ` mean 1 < mean 2, ` at the 99% level, first verify that the measured mean of `sample 1` is less than the mean of `sample 2` and then use
`tTest(sample1, sample2, 0.02) `

Usage Note:
The validity of the test depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The observed array lengths must both be at least 2.
• ` 0 < alpha < 0.5 `

Parameters:
`sample1` - array of sample data values
`sample2` - array of sample data values
`alpha` - significance level of the test
Returns:
true if the null hypothesis can be rejected with confidence 1 - alpha
Throws:
`NullArgumentException` - if the arrays are `null`
`NumberIsTooSmallException` - if the length of the arrays is < 2
`OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
`MaxCountExceededException` - if an error occurs computing the p-value
• #### tTest

`public double tTest(StatisticalSummary sampleStats1,           StatisticalSummary sampleStats2)             throws NullArgumentException,                    NumberIsTooSmallException,                    MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.

The number returned is the smallest significance level at which one can reject the null hypothesis that the two means are equal in favor of the two-sided alternative that they are different. For a one-sided test, divide the returned value by 2.

The test does not assume that the underlying population variances are equal and it uses approximated degrees of freedom computed from the sample data to compute the p-value. To perform the test assuming equal variances, use `homoscedasticTTest(StatisticalSummary, StatisticalSummary)`.

Usage Note:
The validity of the p-value depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The datasets described by the two Univariates must each contain at least 2 observations.

Parameters:
`sampleStats1` - StatisticalSummary describing data from the first sample
`sampleStats2` - StatisticalSummary describing data from the second sample
Returns:
p-value for t-test
Throws:
`NullArgumentException` - if the sample statistics are `null`
`NumberIsTooSmallException` - if the number of samples is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### homoscedasticTTest

`public double homoscedasticTTest(StatisticalSummary sampleStats1,                        StatisticalSummary sampleStats2)                          throws NullArgumentException,                                 NumberIsTooSmallException,                                 MaxCountExceededException`
Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances. To perform a test without the equal variances assumption, use `tTest(StatisticalSummary, StatisticalSummary)`.

The number returned is the smallest significance level at which one can reject the null hypothesis that the two means are equal in favor of the two-sided alternative that they are different. For a one-sided test, divide the returned value by 2.

See `homoscedasticT(double[], double[])` for the formula used to compute the t-statistic. The sum of the sample sizes minus 2 is used as the degrees of freedom.

Usage Note:
The validity of the p-value depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The datasets described by the two Univariates must each contain at least 2 observations.

Parameters:
`sampleStats1` - StatisticalSummary describing data from the first sample
`sampleStats2` - StatisticalSummary describing data from the second sample
Returns:
p-value for t-test
Throws:
`NullArgumentException` - if the sample statistics are `null`
`NumberIsTooSmallException` - if the number of samples is < 2
`MaxCountExceededException` - if an error occurs computing the p-value
• #### tTest

`public boolean tTest(StatisticalSummary sampleStats1,            StatisticalSummary sampleStats2,            double alpha)              throws NullArgumentException,                     NumberIsTooSmallException,                     OutOfRangeException,                     MaxCountExceededException`
Performs a two-sided t-test evaluating the null hypothesis that `sampleStats1` and `sampleStats2` describe datasets drawn from populations with the same mean, with significance level `alpha`. This test does not assume that the subpopulation variances are equal. To perform the test under the equal variances assumption, use `homoscedasticTTest(StatisticalSummary, StatisticalSummary)`.

Returns `true` iff the null hypothesis that the means are equal can be rejected with confidence `1 - alpha`. To perform a 1-sided test, use `alpha * 2`

See `t(double[], double[])` for the formula used to compute the t-statistic. Degrees of freedom are approximated using the Welch-Satterthwaite approximation.

Examples:

1. To test the (2-sided) hypothesis `mean 1 = mean 2 ` at the 95%, use
`tTest(sampleStats1, sampleStats2, 0.05) `
2. To test the (one-sided) hypothesis ` mean 1 < mean 2 ` at the 99% level, first verify that the measured mean of `sample 1` is less than the mean of `sample 2` and then use
`tTest(sampleStats1, sampleStats2, 0.02) `

Usage Note:
The validity of the test depends on the assumptions of the parametric t-test procedure, as discussed here

Preconditions:

• The datasets described by the two Univariates must each contain at least 2 observations.
• ` 0 < alpha < 0.5 `

Parameters:
`sampleStats1` - StatisticalSummary describing sample data values
`sampleStats2` - StatisticalSummary describing sample data values
`alpha` - significance level of the test
Returns:
true if the null hypothesis can be rejected with confidence 1 - alpha
Throws:
`NullArgumentException` - if the sample statistics are `null`
`NumberIsTooSmallException` - if the number of samples is < 2
`OutOfRangeException` - if `alpha` is not in the range (0, 0.5]
`MaxCountExceededException` - if an error occurs computing the p-value