One way Anova

The One way Anova calculates if the mean out of two or more date-set at least one data-set is significant different. The calculation calculates the Anova only from the checked data-sets in the Anova dialogue.

Interpretation

• The smaller the p value is the more likely there is a data-set with a significant difference.
• Develve uses the commonly accepted value of p < 0.05 for significance.
• No significant difference does not mean that the mean of the data-sets are the same!

For a good result the data must

• The data-sets must be normally distributed see Anderson Darling normality test.
  • If the data set is not normally distributed see "What to do with not normally distributed data". The Kruskal-Wallis Test is the not normally distributed counter part of One way anova.
• The samples are independent
• The variation of the data-sets are equal

Colors of the cells

• Green
  No significant difference
• Yellow
  Significant difference
• Orange
  Sample size to small

Formula one way Anova

Between groups

Error

Total

Result

With the F value and the degrees of freedom (DFb and DFe) can the program calculate the p value.

Sample size

The result is the minimum sample size for all the data sets.

Legend

Total mean of all data
Total amount of data points
Amount of columns (data sets)
Amount of rows (data points of one data set)
data point of row i of data-set of column j
Degrees of freedom
Sum of squares
Mean squares
=Max Mean difference in the group

Example

From the selected data-sets there is at least one data-set that is significant different in mean compared to the other data-sets.
But beware of the difference in variation and one data-set that is not normally distributed.

• Normality 0.00 Not OK.
• Levene Variation test 0.00 Not OK.

In this case it is better to use the Kruskal-Wallis Test.

Data file

External links

• http://en.wikipedia.org/wiki/One-way_analysis_of_variance
• http://www.itl.nist.gov/div898/handbook/ppc/section2/ppc231.htm