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What is ANOVA ?
Analysis of variance (ANOVA) is a collection of statistical models and associated estimate processes (such as "variation" among and between groups) that are used to examine variations in means. Ronald Fisher, a statistician, invented ANOVA. ANOVA is based on the law of total variance, which divides observed variance in a variable into components attributed to various causes of variation. ANOVA, in its most basic form, is a statistical test that determines if two or more population means are equal, and so extends the t-test beyond two means. In other words, the ANOVA is used to determine whether two or more means differ.
Terminology for ANOVA
Dependent variable : The object being measured that is hypothesised to be affected by the independent factors is referred to as the dependent variable.
Independent variables : The objects being measured that may have an effect on the dependent variable are known as independent variables.
Null Hypothesis : When there is no difference between the groups or means, the null hypothesis (H0) is used. The null hypothesis will be accepted or rejected based on the results of the ANOVA test.
Alternative Hypothesis : When it is hypothesised that there is a difference between groups and means, an alternative hypothesis (H1) is proposed.
Factors and levels: In ANOVA, an independent variable is referred to as a factor, and it influences the dependent variable. The varied values of the independent variable employed in an experiment are referred to as levels.
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