Table Of Contents

Hypothesis Testing (Z Test » Two Samples) (G Dataflow)

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    Last Modified: January 12, 2018

    Tests hypotheses about the mean of two independent populations whose distributions are at least approximately normal and whose variances are known.

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    standard deviation y

    Standard deviation of sample set y.

    Default: 1

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    standard deviation x

    Standard deviation of sample set x.

    Default: 1

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    sample set x

    Sampled data from population x.

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    sample set y

    Sampled data from population y.

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    delta

    Hypothetical difference between the means of sample set x and sample set y.

    Default: 0

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    significance level

    Probability that this node incorrectly rejects a true null hypothesis.

    Default: 0.05

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    error in

    Error conditions that occur before this node runs.

    The node responds to this input according to standard error behavior.

    Standard Error Behavior

    Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

    error in does not contain an error error in contains an error
    If no error occurred before the node runs, the node begins execution normally.

    If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

    If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

    Default: No error

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    alternative hypothesis

    Hypothesis to accept if this node rejects the null hypothesis that the two populations have a common mean.

    If the null hypothesis is true, the difference between the means of sample set x and sample set y is zero.

    Name Value Description
    mean(x) - mean(y) != delta 0 The difference between the means of population x and population y is not equal to delta.
    mean(x) - mean(y) > delta 1 The difference between the means of population x and population y is greater than delta.
    mean(x) - mean(y) < delta -1 The difference between the means of population x and population y is less than delta.

    Default: mean(x) - mean(y) != delta

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    null hypothesis rejected?

    A Boolean that indicates whether this node rejects the null hypothesis.

    True p value is less than or equal to significance level. This node rejects the null hypothesis and accepts the alternative hypothesis.
    False p value is greater than significance level. This node accepts the null hypothesis and rejects the alternative hypothesis.
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    p value

    Smallest significance level that leads to rejection of the null hypothesis based on the sample sets.

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    confidence interval

    Lower and upper limits for the difference between the means of two populations. confidence interval indicates the uncertainty in the estimate of the true difference of means.

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    low

    Lower limit of the estimate of the difference between the means of two populations.

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    high

    Upper limit of the estimate of the difference between the means of two populations.

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    Z test information

    Sample statistics of the Z test.

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    sample x mean

    Mean of sample set x.

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    sample y mean

    Mean of sample set y.

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    sample mean difference

    Difference between sample x mean and sample y mean.

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    sample x standard deviation

    Standard deviation of sample set x.

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    sample y standard deviation

    Standard deviation of sample set y.

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    sample standard error difference

    Standard error of the difference between sample x mean and sample y mean.

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    sample Z value

    Sample test statistic used in the Z test.

    sample Z value is equal to sample mean difference delta sample standard error difference .

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    Z critical value

    Z value that corresponds to significance level and alternative hypothesis.

    Algorithm for Calculating Z critical value

    Let Zn represent a Z distributed variate with n degrees of freedom. Z critical value satisfies the following equations based on the value of alternative hypothesis.

    alternative hypothesis Z critical value
    mean(x) - mean(y) != delta Prob{Zn > Z critical value} = significance level / 2
    mean(x) - mean(y) > delta Prob{Zn > Z critical value} = significance level
    mean(x) - mean(y) < delta Prob{Zn > Z critical value} = significance level
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    error out

    Error information.

    The node produces this output according to standard error behavior.

    Standard Error Behavior

    Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

    error in does not contain an error error in contains an error
    If no error occurred before the node runs, the node begins execution normally.

    If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

    If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: Not supported

    Web Server: Not supported in VIs that run in a web application


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