Last Modified: January 12, 2018

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

Randomly sampled data from the population.

Hypothesized variance of the population.

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

**Default: **1

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 variance is equal to **variance**.

Name | Value | Description |
---|---|---|

variance(pop) != variance | 0 | The population variance is not equal to variance. |

variance(pop) > variance | 1 | The population variance is greater than variance. |

variance(pop) < variance | -1 | The population variance is less than variance. |

**Default: **variance(pop) != variance

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 variance. **confidence interval** indicates the uncertainty in the estimate of the true population variance.

Lower limit of the estimate of the population variance.

Upper limit of the estimate of the population variance.

Sample statistics of the chi-squared test.

Variance of **sample set**.

Degree of freedom of the chi-squared distribution that the test statistic follows.

Sample test statistic used in the chi-squared test.

**sample chi-squared value** is equal to
$\frac{(n-1)\text{\hspace{0.17em}}*\text{\hspace{0.17em}}\mathrm{sample\; variance}}{\mathrm{variance}}$.

Lower chi-squared value that corresponds to **significance level** and **alternative hypothesis**.

Algorithm for Calculating **chi-squared critical value (lower)**

Let *X*_{n} represent a chi-squared distributed variate with *n* degrees of freedom. **chi-squared critical value (lower)** satisfies the following equations based on the value of **alternative hypothesis**.

alternative hypothesis |
chi-squared critical value (lower) |
---|---|

variance(pop) != variance | Prob{X_{n} < chi-squared critical value (lower)} = significance level / 2 |

variance(pop) > variance | chi-squared critical value (lower) = NaN |

variance(pop) < variance | Prob{X_{n} < chi-squared critical value (lower)} = significance level |

Upper chi-squared value that corresponds to **significance level** and **alternative hypothesis**.

Algorithm for Calculating **chi-squared critical value (upper)**

Let *X*_{n} represent a chi-squared distributed variate with *n* degrees of freedom. **chi-squared critical value (upper)** satisfies the following equations based on the value of **alternative hypothesis**.

alternative hypothesis |
chi-squared critical value (upper) |
---|---|

variance(pop) != variance | Prob{X_{n} > chi-squared critical value (upper)} = significance level / 2 |

variance(pop) > variance | Prob{X_{n} > chi-squared critical value (upper)} = significance level. |

variance(pop) < variance | chi-squared critical value (upper) = NaN |

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