The TwoSampleTTest function computes the two sample t-test upon datasets X1 and X2.  This calculation is used to determine the significance of the difference between sample means and an assumed difference in population means when the standard deviation of the population is unknown.

The first parameter X1 is the first data sample to use in the analysis.

The second parameter X2 is the second data sample to use in the analysis.

The third parameter beta is the assumed difference in population means (assumed population mean of X1 minus the assumed population mean of X2), specified as a real constant.

The options argument can contain one or more of the options shown below.

alternative='twotailed', 'lowertail', or 'uppertail'

This option is used to specify the type or interval used in the analysis, or similarly, the alternative hypothesis to consider when performing the analysis.

confidence=float

This option is used to specify the confidence level of the interval and must be a floating-point value between 0 and 1.  By default this is set to 0.95.

equalvariances=truefalse

This option is used to indicate if either the variance or the standard deviation of the two populations are known to be equal.  Specifying true all allows for a slightly better estimate as a result of the provided information.  By default, this option is false.

ignore=truefalse

This option is used to specify how to handle non-numeric data. If ignore is set to true all non-numeric items in data will be ignored.

output='report', 'statistic', 'pvalue', 'confidenceinterv al', 'distribution', 'hypothesis', or list('statistic', 'pvalue', 'confidenceinterval', 'distribution', 'hypothesis')

This option is used to specify the desired format of the output from the function.  If 'report' is specified then a module containing all output from this test is returned.  If a single parameter name is specified other than 'report' then that quantity alone is returned.  If a list of parameter names is specified then a list containing those quantities in the specified order will be returned.

weights1=rtable

Vector of weights (one-dimensional rtable). If these weights are given, the TwoSampleTTest function will scale each data point in X1 to have given weight. Note that the weights provided must have type realcons and the results are floating-point, even if the problem is specified with exact values. Both the data array and the weights array must have the same number of elements.

weights2=rtable

Vector of weights (one-dimensional rtable).  This parameter is equivalent to the option weights1, except applying to data in X2.

This test generates a complete report of all calculations in the form of a userinfo message.  In order to access this report, specify infolevel[Statistics] := 1.

A stronger version of the t-test, the z-test is available if the standard deviation of the sample is known.

If data samples are paired data (collected as a pair of observations rather than as independent observations), the paired t-test may be used.

Standard T-Test on Two Samples (Unequal Variances)
--------------------------------------------------
Null Hypothesis:
Sample drawn from populations with difference of means equal to 0
Alt. Hypothesis:
Sample drawn from population with difference of means not equal to 0

Sample sizes:            10, 10
Sample means:            7.6, 7.2
Sample standard devs.:   4.24788, 3.48967
Difference in means:     0.4
Distribution:            StudentT(17.3463603321218)
Computed statistic:      0.230089
Computed pvalue:         0.820714
Confidence interval:     -3.26224630470081 .. 4.06224630470081
                         (difference of population means)

Result: [Accepted]
There is no statistical evidence against the null hypothesis

Standard T-Test on Two Samples (Equal Variances)
------------------------------------------------
Null Hypothesis:
Sample drawn from populations with difference of means equal to 0
Alt. Hypothesis:
Sample drawn from population with difference of means not equal to 0

Sample sizes:            10, 10
Sample means:            7.6, 7.2
Sample standard devs.:   4.24788, 3.48967
Difference in means:     0.4
Distribution:            StudentT(18)
Computed statistic:      0.230089
Computed pvalue:         0.820617
Confidence interval:     -3.252356255014 .. 4.05235625501399
                         (difference of population means)

Result: [Accepted]
There is no statistical evidence against the null hypothesis

Standard T-Test on Two Samples (Unequal Variances)
--------------------------------------------------
Null Hypothesis:
Sample drawn from populations with difference of means greater than 0
Alt. Hypothesis:
Sample drawn from population with difference of means less than 0

Sample sizes:            10, 10
Sample means:            7.6, 7.2
Sample standard devs.:   4.24788, 3.48967
Difference in means:     0.4
Distribution:            StudentT(17.3463603321218)
Computed statistic:      0.230089
Computed pvalue:         0.589643
Confidence interval:     -infinity .. 3.42075593333579
                         (difference of population means)

Result: [Accepted]
There is no statistical evidence against the null hypothesis

Standard T-Test on Two Samples (Unequal Variances)
--------------------------------------------------
Null Hypothesis:
Sample drawn from populations with difference of means less than 0
Alt. Hypothesis:
Sample drawn from population with difference of means greater than 0

Sample sizes:            10, 10
Sample means:            7.6, 7.2
Sample standard devs.:   4.24788, 3.48967
Difference in means:     0.4
Distribution:            StudentT(17.3463603321218)
Computed statistic:      0.230089
Computed pvalue:         0.410357
Confidence interval:     -2.62075593333579 .. infinity
                         (difference of population means)

Result: [Accepted]
There is no statistical evidence against the null hypothesis

Kanji, Gopal K. 100 Statistical Tests. London: SAGE Publications Ltd., 1994.

Sheskin, David J. Handbook of Parametric and Nonparametric Statistical Procedures. London: CRC Press, 1997.