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The t-test is very handy when you have two groups to statistically compare. But what happens if you have more than two groups? **Analysis of Variance or ANOVA** is the statistical technique that is analogous to the t-test.

The *ANOVA* is an inferential statistic, a parametric statistic and is very powerful. It can reject the null or find differences among groups - if indeed they exist. The assumptions of homogeneity of variance, equal group sizes and normal distribution of scores should be adhered to - just as the t-test should meet these assumptions.

This statistical technique answers the null hypothesis: There is *no difference* among three or more (3+) groups on their respective mean scores.

There is one Independent variable with three or more (3+) categories. These levels are nominally scaled. There is one Dependent variable that is continuous in its numeric range. This means that interval or ratio scales (where units are exactly the same) are used.

The statistic you obtain to determine statistical significance is the *F ratio* or F statistic.

With ANOVA there are more than two mean scores; so you cannot eyeball the scores and make statistical deductions. You have to conduct a **post hoc procedure** or multiple comparison tests. There are many choices statistical for post hoc procedures to choose from. Each is named after the scientist who developed it.

* Fisher ‘s LSD statistical test

* Duncan’s new multiple range statistical test

* Newman-Keuls statistical test

* Tukey’s HSD statistical test

* Scheffe´ statistical test

**Other ANOVA Statistical Procedures to Choose From**

> **Factorial ANOVA** - ANOVA statistical designs, called factorial ANOVA, compare *more than one* independent variable in dissertation research designs.

> **ANCOVA (Analysis of Covariance)** - The purpose of this statistical technique is to make groups equivalent before they are compared on the dependent variable in doctoral research designs.

> **Repeated Measures ANOVA** -There are occasions when we need to measure something on a recurrent basis. You measure the dependent variable more than once; you repeatedly measure it. This is where the statistical procedure gets its name, *Repeated Measures ANOVA*.

> **MANOVA (Multiple Analysis of Variance)** - The purpose of this statistical technique is to use multiple dependent and independent variables in a doctoral research design.

Return from ANOVA to statistical tests.