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PairedZTest

PairedZTest[data]
tests whether the mean of the data is zero.
PairedZTest
tests whether the means of and are equal.
PairedZTest
tests for zero or equal means assuming a population variance .
PairedZTest
tests the mean against .
PairedZTest
returns the value of .
  • PairedZTest performs a hypothesis test on data with null hypothesis that the true population mean is some value , and alternative hypothesis that .
  • Given and , PairedZTest performs a test on the paired differences of the two datasets.
  • By default a probability value or -value is returned.
  • A small -value suggests that it is unlikely that is true.
  • The data in dspec can be univariate or multivariate .
  • The argument can be any positive real number or a positive definite matrix with dimension equal to the dimension of data.
  • The argument can be a real number or a real vector with length equal to the dimension of the data.
  • PairedZTest assumes that the data is normally distributed and that the variance is known and not estimated from the data.
  • If variances or covariance matrices are not provided, PairedZTest treats the sample estimate as the known variance or covariance.
  • PairedZTest returns a HypothesisTestData object htd that can be used to extract additional test results and properties using the form htd["property"].
  • PairedZTest can be used to directly give the value of .
  • Properties related to the reporting of test results include:
"DegreesOfFreedom"the degrees of freedom of a test
"PValue"list of -values
"PValueTable"formatted table of -values
"TestData"list of pairs of test statistics and -values
"TestDataTable"formatted table of -values and test statistics
"TestStatistic"list of test statistics
"TestStatisticTable"formatted table of test statistics
  • If a known variance is not provided, PairedZTest performs a -test assuming the sample variance is the known variance for univariate data and Hotelling's test assuming the sample covariance is the known covariance for multivariate data.
  • Options include:
AlternativeHypothesis"Unequal"the inequality for the alternative hypothesis
SignificanceLevel0.05cutoff for diagnostics and reporting
VerifyTestAssumptionsAutomaticwhat assumptions to verify
  • For tests of location, a cutoff is chosen such that is rejected if and only if . The value of used for the and properties is controlled by the SignificanceLevel option. This value is also used in diagnostic tests of assumptions including tests for normality, equal variance and symmetry. By default is set to .
"Normality"verify that all data is normally distributed
Test whether the mean of a population is zero:
The full test table:
Test whether the means of two dependent populations differ:
The mean of the differences:
At the level mean of the differenced data is not significantly different from 0:
Compare the locations of dependent multivariate populations:
The mean of the differences:
At the level the mean of the differenced data is not significantly different from 0:
Test whether the mean of a population is zero:
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The full test table:
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Test whether the means of two dependent populations differ:
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The mean of the differences:
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At the level mean of the differenced data is not significantly different from 0:
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Compare the locations of dependent multivariate populations:
In[1]:=
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The mean of the differences:
In[2]:=
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Out[3]=
At the level the mean of the differenced data is not significantly different from 0:
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Test versus :
The -values are typically large when mean is close to :
The -values are typically small when the location is far from :
Using Automatic is equivalent to testing for a mean of zero:
Test versus :
The -values are typically large when the mean is close to :
The -values are typically small when the location is far from :
Test whether the mean vector of a multivariate population is the zero vector:
Alternatively, test against :
Test whether the mean of differenced datasets is zero:
The -values are generally small when the mean is not zero:
The -values are generally large when the mean is zero:
Test whether the mean of differenced data is 3:
The order of the datasets affects the test results:
Test whether the mean vector of differenced multivariate datasets is the zero vector:
Alternatively test against :
Create a HypothesisTestData object for repeated property extraction:
The properties available for extraction:
Extract some properties from a HypothesisTestData object:
The -value and test statistic:
Extract any number of properties simultaneously:
The -value and test statistic:
Tabulate the test results:
Retrieve the entries from a test table for customized reporting:
Tabulate -values or test statistics:
The -value from the table:
The test statistic from the table:
A two-sided test is performed by default:
Test versus :
Perform a two-sided test or a one-sided alternative:
Test versus :
Test versus :
Test versus :
Perform tests with one-sided alternatives when is given:
Test versus :
Test versus :
Set the significance level for diagnostic tests:
By default, is used:
The significance level is also used for and :
By default normality is tested:
Here, normality is assumed:
The result is the same but a warning is issued:
Alternatively, use All:
Bypassing diagnostic tests can save compute time:
It is often useful to bypass diagnostic tests for simulation purposes:
The assumptions of the test hold by design, so a great deal of time can be saved:
The results are identical:
On average a person's arm span is approximately equal to his or her height. A college rowing team was measured with the expectation that their arm spans would be significantly greater than their heights. Assume that the standard deviation for the height and arm span of the adult population is 0.4 ft.:
An estimate of the distribution for the difference in arm span and height:
The rowing team's arm spans are significantly greater than their heights:
You might also be interested in the ratio of arm span to height:
The PairedZTest should not be used to test this since the ratio of two normal random variables is not normal:
PairedZTest is equivalent to a ZTest for a single dataset:
For two datasets the PairedZTest is equivalent to a ZTest of the paired differences:
The known variance:
If the variance of the population is unknown the less powerful PairedTTest should be used:
The probability that the PairedZTest will return a -value smaller than the PairedTTest:
If the data can be paired the PairedZTest is more powerful than the ZTest:
The paired test detects a significant difference where the unpaired test does not:
PairedZTest requires that the data be normally distributed:
Use a median-based test:
If a known variance is not explicitly given, a warning is issued:
A known variance should be specified:
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