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Performing F-Test in R programming – var.test() Method
• Last Updated : 10 May, 2020

With the help of `var.test()` method, we can perform the f-test between two normal population with some hypothesis that variances of two populations are equal in R programming.

Syntax: `var.test()`

Return: Returns the F-Test score for some hypothesis.

Example 1:

 `x <``-` `rnorm(``50``, mean``=``0``)``y <``-` `rnorm(``50``, mean``=``1``)`` ` `# Using var.test() method`` ` `gfg <``-` `var.test(x, y)`` ` `print``(gfg)`

Output:

```F test to compare two variances

data:  x and y
F = 1.0779, num df = 49, denom df = 49, p-value = 0.794
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.6116778 1.8994484
sample estimates:
ratio of variances
1.077892

```

Example 2:

 `x <``-` `rnorm(``50``, mean``=``1.2``)``y <``-` `rnorm(``50``, mean``=``1.8``)`` ` `# Using var.test() method`` ` `gfg <``-` `var.test(x, y)`` ` `print``(gfg)`

Output:

```F test to compare two variances

data:  x and y
F = 2.382, num df = 49, denom df = 49, p-value = 0.002911
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
1.351715 4.197491
sample estimates:
ratio of variances
2.381976

```
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