One-Way Analysis of Variance (ANOVA) is a statistical test that is used to compare two or more means from different groups. It is a parametric test that measures the difference between the means of two or more groups, in order to determine if the difference is statistically significant. The assumption for ANOVA is that the data are normally distributed. ANOVA is used to assess the impact of one independent variable on a dependent variable.
History
One-Way Analysis of Variance (ANOVA) was first developed by the English statistician and geneticist Ronald Fisher in the early 1920s. Fisher developed ANOVA in order to compare the mean of different populations. He proposed the F-test, which is a form of ANOVA, as a way to compare the variances of two populations. This method was later extended to compare the means of more than two populations.
Applications
One-Way Analysis of Variance (ANOVA) is used to compare the means of different population groups. It can be used to test for differences in the means of two or more samples. ANOVA is commonly used in scientific research to determine the effect of a treatment or independent variable on a dependent variable. It can also be used to compare the means of different populations, such as in the analysis of clinical trials.
References
Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Kirk, R. E. (2017). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Thousand Oaks, CA: Sage Publications.
Meyers, L. S., Gamst, G., & Guarino, A. J. (2006). Applied Multivariate Research: Design and Interpretation. Thousand Oaks, CA: Sage Publications.
Wilcox, R. R. (2017). Introduction to Robust Estimation and Hypothesis Testing (4th ed.). Amsterdam: Elsevier Academic Press.