Member-only story
Understanding ANOVA
In this blog, you are going to find answers to the below questions.
- What is ANOVA?
- What are the assumptions of ANOVA?
- In which situations is the ANOVA used?
- What is the difference between Student’s T-test and ANOVA?
What Is ANOVA?
ANOVA is a statistical test that is used to evaluate the difference among the means of three or more groups.
Assumptions of ANOVA
- Populations are normally distributed.
ANOVA can only be applied to data that are normally distributed so we need to run a normality test. However, ANOVA test is robust to the assumption of normality. It works even if the distributions are not perfectly normal.
- Populations have equal variances.
ANOVA can only be applied to samples with the same variance. We need to run a Leven’s test to check the homogeneity of variance.
- Samples are randomly and independently drawn.
We need to check the experimental design to determine if samples are randomly selected.
One Way ANOVA
The one-way ANOVA compares the means between the groups and determines whether any of those means are statistically significantly different from each other. F-ratio (F-statistic, F) is used to analyze the variance between the means.
The Difference Between ANOVA and T-test
The t-test is a method that determines whether two populations are statistically different from each other, whereas ANOVA determines whether three or more populations are statistically different from each other.
Both of them look at the difference in means and the spread of the distributions (i.e., variance) across…
