# One-Way Analysis of Variance (ANOVA)

The most familiar one-way anovas are "fixed effect" or "model I" anovas. The different groups are interesting, and you want to know which are different from each other. As an example, you might compare the AAM length of the mussel species *Mytilus edulis*, *Mytilus galloprovincialis*, *Mytilus trossulus* and *Mytilus californianus*; you'd want to know which had the longest AAM, which was shortest, whether *M. edulis* was significantly different from *M. trossulus*, etc.

## Figure 6-3 One-Way ANOVA dialog with Tukey HSD test selected

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### Click on the Options button in the One-Way ANOVA dialog box.

Several people have put together web pages that will perform a one-way anova; one good one is It is easy to use, and will handle three to 26 groups and 3 to 1024 observations per group. It does not do the Tukey-Kramer test and does not partition the variance.

### Hypothesis 1 Null hypothesis:.ANOVA.

Some versions of Excel include an "Analysis Toolpak," which includes an "Anova: Single Factor" function that will do a one-way anova. You can use it if you want, but I can't help you with it. It does not include any techniques for unplanned comparisons of means, and it does not partition the variance.

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## Oneway ANOVA Explanation and Example in R; ..

If you have only two groups, you can do a This is mathematically equivalent to an anova and will yield the exact same *P* value, so if all you'll ever do is comparisons of two groups, you might as well call them *t*–tests. If you're going to do some comparisons of two groups, and some with more than two groups, it will probably be less confusing if you call all of your tests one-way anovas.

## Usually we would like to reject the null hypothesis

The usual way to graph the results of a one-way anova is with a bar graph. The heights of the bars indicate the means, and there's usually some kind of error bar, either or . Be sure to say in the figure caption what the error bars represent.

## That is the equivalent omnibus test to a traditional Oneway ANOVA.

Analyzing the log-transformed data with one-way anova, the result is *F*_{6,76}=11.72, *P*=2.9×10^{−9}. So there is very significant variation in mean genome size among these seven taxonomic groups of crustaceans.