One Way Anova Hypothesis Example. Reporting the results of a one way anova we found a statistically significant difference in average crop yield according to fertilizer type f 2 9 073 p 0 001. Anova allows one to determine whether the differences between the samples are simply due to random error sampling errors or whether there are systematic treatment effects that causes the mean in one group to differ from the mean in another.
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μ 1 μ 2 μ 3 μ k all the population means are equal h 1 null hypothesis. The null hypothesis for the test is that the two means are equal. This is an example of a two factor anova where the factors are treatment with 5 levels and sex with 2 levels.
To see if there is a statistically significant difference in mean exam scores we can conduct a one way anova.
For example we might want to know if three different studying techniques lead to different mean exam scores. Examples of when to use a one way anova. They don t all have to be different just one of them. Where µ group mean and k number of groups.
