Last Modified: June 25, 2019

Tests hypotheses about the variance of two independent populations whose distributions are at least approximately normal.

Sampled data from population *x*.

Sampled data from population *y*.

Hypothetical quotient between the variances of **sample set x** and **sample set y**.

**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 two populations have a common variance.

If the null hypothesis is true, the variance of the quotient between **sample set x** and **sample set y** is zero.

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

variance(x) / variance(y) != ratio | 0 | The quotient between the variance of population x and population y is not equal to ratio. |

variance(x) / variance(y) > ratio | 1 | The quotient between the variance of population x and population y is greater than ratio. |

variance(x) / variance(y) < ratio | -1 | The quotient between the variance of population x and population y is less than ratio. |

**Default: **variance(x) - variance(y) != ratio

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 ratio. **confidence interval** indicates the uncertainty in the estimate of the true ratio.

Lower limit of the estimate of the ratio.

Upper limit of the estimate of the ratio.

Sample statistics used in the F test.

Variance of **sample set x**.

Variance of **sample set y**.

Quotient of **sample x variance** and **sample y variance**.

Degree of freedom of the first chi-squared variate in the F distribution that the test statistic follows.

Degree of freedom of the second chi-squared variate in the F distribution that the test statistic follows.

Sample test statistic used in the F test.

**sample F value** is equal to
$\frac{\mathrm{sample\; variance\; ratio}}{\mathrm{ratio}}$.

Lower F value that corresponds to **significance level** and **alternative hypothesis**.

Algorithm for Calculating **F critical value (lower)**

Let *F*_{(n1, n2)} represent an F distributed variate with *n*1 and *n*2 degrees of freedom. **F critical value (lower)** satisfies the following equations based on the value of **alternative hypothesis**.

alternative hypothesis |
F critical value (lower) |
---|---|

variance(x) / variance(y) != ratio | Prob{F_{(n1, n2)} < F critical value (lower)} = significance level / 2 |

variance(x) / variance(y) > ratio | F critical value (lower) = NaN |

variance(x) / variance(y) < ratio | Prob{F_{(n1, n2)} < F critical value (lower)} = significance level |

Upper F value that corresponds to **significance level** and **alternative hypothesis**.

Algorithm for Calculating **F critical value (upper)**

Let *F*_{(n1, n2)} represent an F distributed variate with *n*1 and *n*2 degrees of freedom. **F critical value (upper)** satisfies the following equations based on the value of **alternative hypothesis**.

alternative hypothesis |
F critical value (upper) |
---|---|

variance(x) / variance(y) != ratio | Prob{F_{(n1, n2)} > F critical value (upper)} = significance level / 2 |

variance(x) / variance(y) > ratio | Prob{F_{(n1, n2)} > F critical value (upper)} = significance level |

variance(x) / variance(y) < ratio | F critical value (lower) = 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