Difference Between ANCOVA and ANOVA

ANCOVA and ANOVA are two statistical techniques for matching samples or groups on one or more than one variable. They are used to perform the same function but the method adopted is different. ANCOVA is more robust and unbiased compared to ANOVA. ANCOVA is exactly like ANOVA, except that the effects of a third variable are statistically “controlled for.” ANCOVA only uses a general linear model, while ANOVA uses both linear and nonlinear models.

What is ANCOVA?

ANCOVA is a statistical technique used to match samples or groups on one or more than one variable. ANCOVA stands for “Analysis of Covariance.” It is an analysis technique that has two or more variables. The variables involved in ANCOVA must be at least one continuous and one categorical predictor variable. It is a test method to test the effect of the outcome variable after removing the variance. It uses covariate to improve its statistical power. ANCOVA implies that there is a linear relationship between dependent and independent variables.

What is ANOVA?

ANOVA is a statistical technique used to equate samples or groups on one or more than one variable. ANOVA stands for “Analysis of Variance” in statistics. It is tested to verify the presence of a common mean between several groups. It is quite a useful test compared to t-tests for such purposes. There are different types of ANOVA including ½ one-way ANOVA, ½ factorial ANOVA, ½ repeated measures ANOVA, and MANOVA.

Key differences

1. ANCOVA uses covariate while ANOVA does not use covariate.
2. A distinctive feature of ANOVA is BG, while in an ANCOVA case, BG is divided into TX and COV variation.
3. Both ANOVA and ANCOVA use WG variation. In ANCOVA WG, the variation is divided by individual differences as COV, while ANOVA uses it only for individual characteristics.
4. ANCOVA is more robust and unbiased compared to ANOVA.
5. ANCOVA is exactly like ANOVA, except that the effects of a third variable are statistically “controlled for.”
6. ANCOVA only uses a general linear model, while ANOVA uses both linear and nonlinear models.