Version:

Last Modified: January 12, 2018

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

Standard deviation of **sample set**.

**Default: **1

Randomly sampled data from the population.

Hypothesized mean of the population.

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

**Default: **0

Probability that this node incorrectly rejects a true null hypothesis.

**Default: **0.05

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.

**Default: **No error

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

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. |

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

Lower and upper limits for the population mean. **confidence interval** indicates the uncertainty in the estimate of the true population mean.

Lower limit of the estimate of the population mean.

Upper limit of the estimate of the population mean.

Sample statistics of the Z test.

Mean of **sample set**.

Standard deviation of **sample set**.

Standard error of the mean of **sample set**.

Sample test statistic used in the Z test.

**sample Z value** is equal to
$\frac{\mathrm{sample\; mean}\text{\hspace{0.17em}}-\text{\hspace{0.17em}}\mathrm{mean}}{\mathrm{sample\; standard\; error\; mean}}$.

*Z* value that corresponds to **significance level** and **alternative hypothesis**.

Algorithm for Calculating **Z critical value**

Let *Z*_{n} 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(pop) != mean | Prob{Z_{n} > Z critical value} = significance level / 2 |

mean(pop) > mean | Prob{Z_{n} > Z critical value} = significance level |

mean(pop) < mean | Prob{Z_{n} > Z critical value} = significance level |

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

**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.

**Where This Node Can Run: **

Desktop OS: Windows

FPGA: Not supported

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