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# Difference Between One Way ANOVA And Two Way ANOVA

When talking about research in the field of Science or Social science, whether it is Biology, Business, Economics, Psychology, Sociology, or any other subject, the Analysis of Variance (ANOVA) is an important statistical tool for analysing the data. The tool is used to compare and analyse the results of laboratories when more than one factor can be of influence and must be distinguished from random effects. Two folds of the technique lead the comparison; i.e., one way ANOVA and Two-way ANOVA.

ANOVA analysis the statistics on the basis of the hypothesis, either null or an alternate hypothesis. Since a hypothesis is an educated guess of the possible results of the cause-and-effect relationship, it will either result for the cause or against the purpose. The null hypothesis in ANOVA is valid when all the sample means don’t have a significant difference. Similarly, the alternate hypothesis is valid when at least one of the sample mean is different from the rest of the sample means.

As the names indicate of the two-fold techniques, the researcher takes only one factor in one way ANOVA and the researcher investigate two factors simultaneously in two way ANOVA. The former one is a hypothetical test, testing one-factor using variance whereas the later one is a statistical technique studying the influencing variables. However, the independent variables of both the types are proportional to the ways in their names.

One way ANOVA is based on the assumption of normal distribution of the sample population, the ratio level of the dependent variables, the independence of the samples, and the variance of the population. While two way ANOVA is also based on the assumption of normal distribution of the sample population but the measurement of the dependent variable is at a continuous level, unlike the variation in one way ANOVA. The two way ANOVA studies the inter-relationship between the influence of independent variables on dependent variables.

Two way ANOVA is often taken as an extended version of one way ANOVA as the former one has many advantages in the comparison of the later one.