Variation F-test

To calculate if there is a significant difference between variation of the two datasets. It calculates the F-test between this column and the comparing column.

Interpretation

The smaller the p value is the more likely there is a significant difference between the variation in the 2 data sets.
In Develve for variation a significant difference the p value must be below 0.05.
For a good F-test the datasets must be normally distributed see Anderson Darling normality test.
For a good power (0.8 in Develve) the sample size for both data sets must be bigger than the minimum sample size calculated.

Options

Bigger/Smaller (one sided test) or not equal test (two sided)

Colors of the cells

Green No significant difference
Yellow Significant difference
Red Not normally distributed
Orange Sample size to small

Formula Calculating F value

Where s1 is the smallest of the comparing STDEV values.

With the F value and the degrees of freedom can the program interpolate in a table the p value.

Sample size

not equal

This results in the degrees of freedom out of the formula

table, the Minimum sample size is formula

bigger smaller

This results in the degrees of freedom out of the formula

table, the Minimum sample size is formula

Legend

n = n

= STDEV smallest variation
formula

= STDEV biggest variation
formula

Degrees of freedom

Example

Select Variation test. To use the F-test test first unselect "non normal distributed" when the box is selected the Levene test is calculated. Then select Diff variation.

The difference in variance between data set A and B is not significant (Row F test p >0.05) and the sample size is to small (Row min Samples 240).
The difference in variance between data set A and C is significant (Row F test p <0.05) and the sample size is big enough.
The difference in variance between data set A and D is not significant (Row F test p >0.05) and the sample size is big enough.

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