The OneSampleTTest function computes the one sample t-test on a dataset X.  This calculation is used to determine the significance of the difference between the sample mean and an assumed population mean when the standard deviation of the population is unknown.

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

The second parameter mu0 is the assumed population mean, specified as a real constant.

The OneSampleTTest[SampleSize] utility computes the number of samples required in a data set in order to get a confidence interval with the specified width using this test.

The first parameter of the utility, width, specifies the desired width of the confidence interval (difference between the upper bound and the lower bound).  This value must be strictly greater than 0.

The second parameter of the utility, sigma, specifies a worst-case estimate on the standard deviation of the sample size.

The test_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.

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 X will be ignored.

output='report', 'statistic', 'pvalue', 'confidenceinterval', '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.

summarize= 'true', 'false', 'embed'

This option controls the display of a printed or embedded summary for the hypothesis test. Unlike the output option, the displayed summary is not assignable output.

weights=rtable

Vector of weights (one-dimensional rtable). If weights are given, the OneSampleTTest function will scale each data point 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.

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

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.

iterations=posint -- This option specifies the maximum number of iterations to process when attempting to calculate the number of samples required.  By default this is set to 100.

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 or use the summarize option.

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

$\mathrm{with}\left(\mathrm{Statistics}\right)\:$

$X≔\mathrm{Array}\left(\left[9\,10\,8\,4\,8\,3\,0\,10\,15\,9\right]\right)\:$

$\mathrm{Mean}\left(X\right)$

$7.60000000000000$

$\mathrm{OneSampleTTest}\left(X\,5\,\mathrm{confidence}=0.95\,\mathrm{summarize}=\mathrm{embed}\right)\:$

$\mathrm{OneSampleTTest}\left(X\,5\,\mathrm{confidence}=0.95\,\mathrm{alternative}='\mathrm{lowertail}'\,\mathrm{summarize}=\mathrm{true}\right)$

Standard T-Test on One Sample

-----------------------------

Null Hypothesis:
Sample drawn from population with mean greater than 5

Alt. Hypothesis:
Sample drawn from population with mean less than 5

Sample Size:             10

Sample Mean:             7.6

Sample Standard Dev.:    4.24788

Distribution:            StudentT(9)

Computed Statistic:      1.93553750114025

Computed p-value:        .957542459363412

Confidence Interval:     -infinity .. 10.0624132658958

                         (population mean)

Result: [Accepted]
This statistical test does not provide enough evidence to conclude that the null hypothesis is false.

$\mathrm{hypothesis}=\mathrm{true},\mathrm{confidenceinterval}=-\mathrm{\infty }..10.0624132658958,\mathrm{distribution}=\mathrm{StudentT}\left(9\right),\mathrm{pvalue}=0.957542459363412,\mathrm{statistic}=1.93553750114025$

$\mathrm{infolevel}\left[\mathrm{Statistics}\right]≔1\:$

$\mathrm{OneSampleTTest}\left(X\,5\,\mathrm{confidence}=0.95\,\mathrm{alternative}='\mathrm{uppertail}'\right)$

Standard T-Test on One Sample

-----------------------------

Null Hypothesis:
Sample drawn from population with mean less than 5

Alt. Hypothesis:
Sample drawn from population with mean greater than 5

Sample Size:             10

Sample Mean:             7.6

Sample Standard Dev.:    4.24788

Distribution:            StudentT(9)

Computed Statistic:      1.93553750114025

Computed p-value:        .0424575406365881

Confidence Interval:     5.1375867341042 .. infinity

                         (population mean)

Result: [Rejected]
This statistical test provides evidence that the null hypothesis is false.

$\mathrm{hypothesis}=\mathrm{false},\mathrm{confidenceinterval}=5.13758673410420..\mathrm{\infty },\mathrm{distribution}=\mathrm{StudentT}\left(9\right),\mathrm{pvalue}=0.0424575406365881,\mathrm{statistic}=1.93553750114025$

$\mathrm{OneSampleTTest}\left[\mathrm{SampleSize}\right]\left(3\,5\right)$

$46$

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.

The Statistics[OneSampleTTest] command was updated in Maple 2016.

The summarize option was introduced in Maple 2016.

For more information on Maple 2016 changes, see Updates in Maple 2016.