Analysis of Variance
Analysis of variance (ANOVA) is a method used to measure the differences between separate variables. The data of the measurement are often given in a bell-shaped or normal curve. ANOVA can be applied to more than two groups of variables. Moreover, these variations may be systematic as the variations in one group cause the variations in another.
Furthermore, these variations may act in combination or in isolation. However, there may be a residual variation which is impossible to explain with the help of any variables. This parametric procedure is often applied to calculate the total variation. Then, it calculates the difference of variations which can be explained by other variables and calculating the residual variation. The comparison between the explained variation and the residual variation shows if the result is statistically important.
ANOVA is often used to study the variability within the established control group. The researcher does the tests and observations in the control group in order to compare the results and see the variability based on some issue. In addition, the analysis of variance can be applied to the studies of the answers of people of different ethnic groups and nationalities. The test is rejected or repeated if the statistical significance is no more than 5% and when the same result would appear 95%. Some researchers reject and repeat the test if the standard is 1% and the difference is 99%.
ANOVA can be used in various types of tests. Among them are: analysis of covariance, multiple regression, N-Way analysis of variance and one-way analysis of variance. Furthermore, ANOVA can be applied to the studies of the influence of one or more independent variables on a dependent one. Finally, it is a widely used procedure and is often applied to statistical measurements. It is the best method to observe and study two or more control groups.
Gelman, A. (2006). Analysis of variance. Retrieved from
Picciano, Anthony G. Analysis of Variance Web. Hunter College and The Graduate Center CUNY. Retrieved from