## ANCOVA vs ANOVA: Learn the Difference

The debate on ANCOVA vs. ANOVA has gained more complexity in the statistical analysis due to each method’s diverse usefulness in testing hypotheses and analyzing variables. Both tools are useful to perform specific statistical functions. However, knowing when to use ANCOVA versus when to apply ANOVA can significantly impact the data quality and interpretation. As a result, most students seek help from experts who know when to use specific data analysis tools. In this blog, our statistics experts will expound on the factors to consider when choosing between ANCOVA and ANOVA. They also help with statistics assignments, projects, and exams.

## What is ANCOVA?

ANCOVA (Analysis of Covariance) is a variance analysis (ANOVA) and regression blend. ANCOVA resembles factorial ANOVA since it can reveal the additional information you will get by considering an independent variable at a time. ANCOVA is more useful in discovering the variance changes of dependent variables following the difference in the covariate variable. Besides, the findings from variance changes are determined following the alteration in the qualitative variable levels. Thus, ANCOVA minimizes uncertain changes in the variance of the dependent variable (error), ensures pure results, and increases the analytical power.

## What is ANOVA?

ANOVA (Analysis of variance) refers to the test for differences among the population means. Such tests examine the variation occurring in each sample relative to the sample variation. The variance analysis is often useful when testing a hypothesis that the means of two or more populations are equal. While conducting regression research, analysts use the ANOVA test when determining how the independent variable impacts the dependent variable. The results from ANOVA tests are useful to prove or disapprove an occurrence of statistically significant differences between the compared means.

## ANCOVA vs ANOVA – What is the Difference?

The primary difference between ANCOVA and ANOVA comes in their applications. For example, the crucial difference is in ANCOVA’s potential to control the impacts of specific variables. ANOVA does not account for these covariates.

ANOVA is most effective for comparing the means of different groups to determine if they originate from the same population. On the contrary, ANCOVA is useful for adjusting the dependent variable for one or more covariates to eliminate the influence of confounding variables. As a result, ANCOVA gives the analysts a better understanding of the relationship between dependent and independent variables. Table 1 is an illustration of the difference between ANCOVA and ANOVA.

Table 1: Comparative Table ANCOVA vs ANOVA

## What are the Similarities Between ANCOVA and ANOVA?

There are four notable similarities between these two statistical tools. These are as follows:

• Both methods entail the analysis of means of multiple groups.
• Both statistical tools accept categorical variables.
• ANCOVA contains the application of ANOVA.
• Ronal Aylmer Fisher, the British statistician, pioneered both tests.