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

As in all hypothesis tests, the hypotheses are statements about the population, not about the sample set.


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Inputs/Outputs

  • c1ddbl.png sample set —

    sample set specifies the randomly sampled data from the population of interest.

  • cdbl.png mean —

    mean specifies the hypothesized mean value of the population. The null hypothesis is that the population mean is equal to mean.

    The mean of a normal distribution is a value of a normal random variable such that the probability that an observed value of the variable is less than or equal to the mean and the probability that the observed value is greater than or equal to the mean are both 50%.

  • cdbl.png significance level —

    significance level specifies the probability that the hypothesis test conclusion is wrong based on the sample set and the hypothesized mean.

  • cdbl.png std —

    std specifies the known standard deviation of the population.

  • ci32.png alternative hypothesis —

    alternative hypothesis specifies the hypothesis to accept if LabVIEW rejects the null hypothesis that the population mean is equal to mean.

    –1mean(pop) < mean—The population mean is less than mean.
    0mean(pop) != mean—The population mean is not equal to mean.
    1mean(pop) > mean—The population mean is greater than mean.
  • ibool.png reject null hypothesis —

    reject null hypothesis indicates whether to reject the null hypothesis, with significance level being the probability of reaching the wrong conclusion.

    If p value is less than or equal to significance level, reject null hypothesis returns TRUE. Reject the null hypothesis and accept the alternative hypothesis. If p value is greater than significance level, reject null hypothesis returns FALSE. Accept the null hypothesis and reject the alternative hypothesis.

  • idbl.png p value —

    p value returns the probability that you incorrectly rejected the null hypothesis.

  • inclst.png confidence interval —

    confidence interval returns a lower and upper limit for the population mean, indicating the uncertainty in the estimate of the true population mean.

  • idbl.png low —

    low returns the lower limit of the estimate of the population mean.

  • idbl.png high —

    high returns the upper limit of the estimate of the population mean.

  • ii32.png error —

    error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

  • inclst.png z test info —

    z test info returns the sample statistics used in the computation of the Z test.

  • idbl.png sample mean —

    sample mean returns the sample mean of the sample set.

  • idbl.png sample std —

    sample std returns the sample standard deviation of the sample set.

  • idbl.png sample se mean —

    sample se mean returns the standard error mean of the sample set.

  • idbl.png sample z value —

    sample z value returns the sample test statistic used in the Z test and is equal to (sample mean – mean)/(sample se mean).

  • idbl.png z value —

    z value returns the Z value that corresponds to the significance level and the alternative hypothesis.