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ANOVA Basic Definition

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# ANOVA Basic Definition

ANOVA, Analysis of Variance, is a large group of techniques that analyzes observed variance and break it down into components. The different components are the sources of variance. ANOVA is generally used to determine to determine if two or more means are equal. ANOVA performs analysis on the variance of two or more data sets to determine if the data sets have the same means. ANOVA calculates whether or not two or more means are within a certain percent chance (confidence level) of being the same based upon the actual and expected levels of variance within each data set.

ANOVA is normally used to test three or more means. If a variance test is being performed on only two means, the desired test would be the Students two-sample t- test. Performing the Students two-sample t-test multiple times on more than two means could result in a much higher probability of type 1 error (rejecting the null hypothesis when it is actually true - that is - assuming that the means are different when they are not). For this reason, a single ANOVA test is preferable when comparing more than two means. If only two means are being analyzed, the F test performed by ANOVA is equivalent to the t-test.

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