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Home Statistical Tests ANOVA Combination of Several Distributions  
See also: ANOVA  
Combination of Several DistributionsWhat happens if the distributions of several samples are considered as individual distributions at one time, and as a cumulative sample (combining all observations of the various samples into one big sample), at another time? Is the distribution of the combined sample different to the individual distributions? If Yes, how do they differ? The answer to this question is important for the understanding of the analysis of variance (ANOVA).
For further discussion we have to discriminate two cases: (1) the means of the individual samples are equal, and (2) the means of the individual samples differ from each other (to be specific, the mean of at least one sample is different to the other means).
If the means of the individual samples are equal (according to our assumptions, the variances are equal anyway), the parameters of the combined distributions are the same as the parameters of the idividual ones. The variance of the combined sample is equal to the variances of the individual samples.
If we combine the individual samples (whose means are not equal) into a larger collective sample the variance of the new sample will be greater than the variance of the individual samples (which is the same for all of them).
This increase in variance of the combined sample gives us now the opportunity to test for the equality of the means of the individual samples, simply by comparing the variances of the individual samples to the variance of the collective sample. Remember, if and only if the means are equal the variance of the collective sample will be the same as the variance of the individual samples. This principle  the testing for the equality of means by investigating the collective variance  is the basis of the ANOVA


Home Statistical Tests ANOVA Combination of Several Distributions 
Last Update: 20121008