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java.lang.Objectorg.apache.commons.math.stat.inference.TTestImpl
public class TTestImpl
Implements t-test statistics defined in the TTest
interface.
Uses commons-math TDistribution
implementation to estimate exact p-values.
Field Summary | |
---|---|
private TDistribution |
distribution
Distribution used to compute inference statistics. |
Constructor Summary | |
---|---|
TTestImpl()
Default constructor. |
|
TTestImpl(TDistribution t)
Create a test instance using the given distribution for computing inference statistics. |
Method Summary | |
---|---|
private void |
checkSampleData(double[] data)
Check sample data. |
private void |
checkSampleData(StatisticalSummary stat)
Check sample data. |
private void |
checkSignificanceLevel(double alpha)
Check significance level. |
protected double |
df(double v1,
double v2,
double n1,
double n2)
Computes approximate degrees of freedom for 2-sample t-test. |
double |
homoscedasticT(double[] sample1,
double[] sample2)
Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances. |
protected double |
homoscedasticT(double m1,
double m2,
double v1,
double v2,
double n1,
double n2)
Computes t test statistic for 2-sample t-test 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. |
protected double |
homoscedasticTTest(double m1,
double m2,
double v1,
double v2,
double n1,
double n2)
Computes p-value for 2-sided, 2-sample t-test, under the assumption of equal subpopulation variances. |
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 . |
void |
setDistribution(TDistribution value)
Modify the distribution used to compute inference statistics. |
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. |
protected double |
t(double m,
double mu,
double v,
double n)
Computes t test statistic for 1-sample t-test. |
protected double |
t(double m1,
double m2,
double v1,
double v2,
double n1,
double n2)
Computes t test statistic for 2-sample t-test. |
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 . |
protected double |
tTest(double m,
double mu,
double v,
double n)
Computes p-value for 2-sided, 1-sample t-test. |
protected double |
tTest(double m1,
double m2,
double v1,
double v2,
double n1,
double n2)
Computes p-value for 2-sided, 2-sample t-test. |
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 |
---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
---|
private TDistribution distribution
Constructor Detail |
---|
public TTestImpl()
public TTestImpl(TDistribution t)
t
- distribution used to compute inference statistics.Method Detail |
---|
public double pairedT(double[] sample1, double[] sample2) throws java.lang.IllegalArgumentException, MathException
t(double, double[])
, with
mu = 0
and the sample array consisting of the (signed)
differences between corresponding entries in sample1
and
sample2.
Preconditions:
pairedT
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if the statistic can not be computed do to a
convergence or other numerical error.public double pairedTTest(double[] sample1, double[] sample2) throws java.lang.IllegalArgumentException, MathException
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:
pairedTTest
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic boolean pairedTTest(double[] sample1, double[] sample2, double alpha) throws java.lang.IllegalArgumentException, MathException
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:
0 < alpha < 0.5
pairedTTest
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data valuesalpha
- significance level of the test
java.lang.IllegalArgumentException
- if the preconditions are not met
MathException
- if an error occurs performing the testpublic double t(double mu, double[] observed) throws java.lang.IllegalArgumentException
This statistic can be used to perform a one sample t-test for the mean.
Preconditions:
t
in interface TTest
mu
- comparison constantobserved
- array of values
java.lang.IllegalArgumentException
- if input array length is less than 2public double t(double mu, StatisticalSummary sampleStats) throws java.lang.IllegalArgumentException
sampleStats
to mu
.
This statistic can be used to perform a one sample t-test for the mean.
Preconditions:
observed.getN() > = 2
.
t
in interface TTest
mu
- comparison constantsampleStats
- DescriptiveStatistics holding sample summary statitstics
java.lang.IllegalArgumentException
- if the precondition is not metpublic double homoscedasticT(double[] sample1, double[] sample2) throws java.lang.IllegalArgumentException
t(double[], double[])
.
This statistic can be used to perform a (homoscedastic) two-sample t-test to compare sample means.
The t-statisitc 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:
homoscedasticT
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not metpublic double t(double[] sample1, double[] sample2) throws java.lang.IllegalArgumentException
homoscedasticT(double[], double[])
.
This statistic can be used to perform a two-sample t-test to compare sample means.
The t-statisitc 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:
t
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not metpublic double t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) throws java.lang.IllegalArgumentException
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-statisitc 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:
t
in interface TTest
sampleStats1
- StatisticalSummary describing data from the first samplesampleStats2
- StatisticalSummary describing data from the second sample
java.lang.IllegalArgumentException
- if the precondition is not metpublic double homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) throws java.lang.IllegalArgumentException
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-statisitc 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:
homoscedasticT
in interface TTest
sampleStats1
- StatisticalSummary describing data from the first samplesampleStats2
- StatisticalSummary describing data from the second sample
java.lang.IllegalArgumentException
- if the precondition is not metpublic double tTest(double mu, double[] sample) throws java.lang.IllegalArgumentException, MathException
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:
tTest
in interface TTest
mu
- constant value to compare sample mean againstsample
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic boolean tTest(double mu, double[] sample, double alpha) throws java.lang.IllegalArgumentException, MathException
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:
sample mean = mu
at
the 95% level, use tTest(mu, sample, 0.05)
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:
tTest
in interface TTest
mu
- constant value to compare sample mean againstsample
- array of sample data valuesalpha
- significance level of the test
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error computing the p-valuepublic double tTest(double mu, StatisticalSummary sampleStats) throws java.lang.IllegalArgumentException, MathException
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:
tTest
in interface TTest
mu
- constant value to compare sample mean againstsampleStats
- StatisticalSummary describing sample data
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic boolean tTest(double mu, StatisticalSummary sampleStats, double alpha) throws java.lang.IllegalArgumentException, MathException
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:
sample mean = mu
at
the 95% level, use tTest(mu, sampleStats, 0.05)
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:
tTest
in interface TTest
mu
- constant value to compare sample mean againstsampleStats
- StatisticalSummary describing sample data valuesalpha
- significance level of the test
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic double tTest(double[] sample1, double[] sample2) throws java.lang.IllegalArgumentException, MathException
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:
tTest
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic double homoscedasticTTest(double[] sample1, double[] sample2) throws java.lang.IllegalArgumentException, MathException
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:
homoscedasticTTest
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data values
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic boolean tTest(double[] sample1, double[] sample2, double alpha) throws java.lang.IllegalArgumentException, MathException
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:
mean 1 = mean 2
at
the 95% level, use
tTest(sample1, sample2, 0.05).
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:
0 < alpha < 0.5
tTest
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data valuesalpha
- significance level of the test
java.lang.IllegalArgumentException
- if the preconditions are not met
MathException
- if an error occurs performing the testpublic boolean homoscedasticTTest(double[] sample1, double[] sample2, double alpha) throws java.lang.IllegalArgumentException, MathException
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:
mean 1 = mean 2
at
the 95% level, use tTest(sample1, sample2, 0.05).
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:
0 < alpha < 0.5
homoscedasticTTest
in interface TTest
sample1
- array of sample data valuessample2
- array of sample data valuesalpha
- significance level of the test
java.lang.IllegalArgumentException
- if the preconditions are not met
MathException
- if an error occurs performing the testpublic double tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) throws java.lang.IllegalArgumentException, MathException
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. 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:
tTest
in interface TTest
sampleStats1
- StatisticalSummary describing data from the first samplesampleStats2
- StatisticalSummary describing data from the second sample
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic double homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) throws java.lang.IllegalArgumentException, MathException
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:
homoscedasticTTest
in interface TTest
sampleStats1
- StatisticalSummary describing data from the first samplesampleStats2
- StatisticalSummary describing data from the second sample
java.lang.IllegalArgumentException
- if the precondition is not met
MathException
- if an error occurs computing the p-valuepublic boolean tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha) throws java.lang.IllegalArgumentException, MathException
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:
mean 1 = mean 2
at
the 95%, use
tTest(sampleStats1, sampleStats2, 0.05)
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:
0 < alpha < 0.5
tTest
in interface TTest
sampleStats1
- StatisticalSummary describing sample data valuessampleStats2
- StatisticalSummary describing sample data valuesalpha
- significance level of the test
java.lang.IllegalArgumentException
- if the preconditions are not met
MathException
- if an error occurs performing the testprotected double df(double v1, double v2, double n1, double n2)
v1
- first sample variancev2
- second sample variancen1
- first sample nn2
- second sample n
protected double t(double m, double mu, double v, double n)
m
- sample meanmu
- constant to test againstv
- sample variancen
- sample n
protected double t(double m1, double m2, double v1, double v2, double n1, double n2)
Does not assume that subpopulation variances are equal.
m1
- first sample meanm2
- second sample meanv1
- first sample variancev2
- second sample variancen1
- first sample nn2
- second sample n
protected double homoscedasticT(double m1, double m2, double v1, double v2, double n1, double n2)
m1
- first sample meanm2
- second sample meanv1
- first sample variancev2
- second sample variancen1
- first sample nn2
- second sample n
protected double tTest(double m, double mu, double v, double n) throws MathException
m
- sample meanmu
- constant to test againstv
- sample variancen
- sample n
MathException
- if an error occurs computing the p-valueprotected double tTest(double m1, double m2, double v1, double v2, double n1, double n2) throws MathException
Does not assume subpopulation variances are equal. Degrees of freedom are estimated from the data.
m1
- first sample meanm2
- second sample meanv1
- first sample variancev2
- second sample variancen1
- first sample nn2
- second sample n
MathException
- if an error occurs computing the p-valueprotected double homoscedasticTTest(double m1, double m2, double v1, double v2, double n1, double n2) throws MathException
The sum of the sample sizes minus 2 is used as degrees of freedom.
m1
- first sample meanm2
- second sample meanv1
- first sample variancev2
- second sample variancen1
- first sample nn2
- second sample n
MathException
- if an error occurs computing the p-valuepublic void setDistribution(TDistribution value)
value
- the new distributionprivate void checkSignificanceLevel(double alpha) throws java.lang.IllegalArgumentException
alpha
- significance level
java.lang.IllegalArgumentException
- if significance level is out of boundsprivate void checkSampleData(double[] data) throws java.lang.IllegalArgumentException
data
- sample data
java.lang.IllegalArgumentException
- if there is not enough sample dataprivate void checkSampleData(StatisticalSummary stat) throws java.lang.IllegalArgumentException
stat
- statistical summary
java.lang.IllegalArgumentException
- if there is not enough sample data
|
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