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Hypothesis Testing (T Test » Single Sample) (G Dataflow)

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    Last Modified: September 4, 2017

    Tests hypotheses about the mean of a population whose distribution is at least approximately normal but whose variance is unknown.

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    sample set

    Randomly sampled data from the population.

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    mean

    Hypothesized mean of the population.

    The null hypothesis is that the population mean is equal to mean.

    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 population mean is equal to mean.

    Name Value Description
    mean(pop) != mean 0 The population mean is not equal to mean.
    mean(pop) > mean 1 The population mean is greater than mean.
    mean(pop) < mean -1 The population mean is less than mean.

    Default: mean(pop) != mean

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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 population mean. confidence interval indicates the uncertainty in the estimate of the true population mean.

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    low

    Lower limit of the estimate of the population mean.

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    high

    Upper limit of the estimate of the population mean.

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    t test information

    Sample statistics of the Student's t test.

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    sample mean

    Mean of sample set.

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    sample standard deviation

    Standard deviation of sample set.

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    sample standard error mean

    Standard error of the mean of sample set.

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    degree of freedom

    Degree of freedom of the Student's t distribution that the test statistic follows.

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    sample t value

    Sample test statistic used in the Student's t test.

    sample t value is equal to sample mean mean sample standard error mean .

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    t critical value

    Student's t value that corresponds to significance level and alternative hypothesis.

    Algorithm for Calculating t critical value

    Let Tn represent a student's t distributed variate with n degrees of freedom. t critical value satisfies the following equations based on the value of alternative hypothesis.

    alternative hypothesis t critical value
    mean(pop) != mean Prob{Tn > t critical value} = significance level / 2
    mean(pop) > mean Prob{Tn > t critical value} = significance level
    mean(pop) < mean Prob{Tn > t 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: This product does not support FPGA devices

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


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