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ANOVA stands for Analysis of Variance. ANOVA belongs to a family of statistical models known as the General Linear Model. It is a widely used statistical technique employed to identify the significant differences between the means of two or more independent groups. ANOVA calculates the differences and tests the hypotheses by finding the variation among and between groups.

ANOVA works on the assumption that the data follows a normal distribution, homogeneous variance and independence of observations. ANOVA establishes three basic assumptions which must be met before proceeding with the tests. These assumptions include the independence of observations, normality of data, and the homogeneity of variances.

In ANOVA, the data is classified into different groups based on a particular variable, and the analysis mainly deals with the sum of squares of observations for individual groups. The sum of squares for groups (Ssg) and the total sum of squares (Sst) are used to calculate the variation between and within the groups. These are compared using an analysis of variance table.

ANOVA tends to be a very powerful statistical technique when compared with the older techniques such as t-test. The non parametric methods should be selected, if the data fails to meet the assumptions mentioned above. ANOVA also has various forms such as two-way ANOVA, ANCOVA (Analysis of Covariance), and Repeated measures ANOVA. These various forms of ANOVA assist in conducting complex statistical analysis to meet varying needs of the researchers.

ANOVA is used in various fields such as business, psychology, economics, and biostatistics. ANOVA plays a vital role in conclusive research by comparing different samples systematically. The efficiency of ANOVA relies heavily on the proper performance of the assumptions and understanding the conditions under which the test should be used.