Explain whether the F test is statistically significant.

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Explain whether the F test is statistically significant.

Explain whether the F test is statistically significant.In Week 4, you explored t-tests, which allowed you to compare a sample to a population or compare two groups to one another. For example, you might want to compare the effectiveness of two types of treatment for insomnia, but what if you want to compare the effectiveness of more than two groups? A one-way analysis of variance, or ANOVA, allows you to do just that—compare multiple groups (called levels) of one independent variable (called a factor). For example, in a study about insomnia treatments, you might compare a level that receives muscle relaxation training to a second level that receives visualization training and, finally, to a third level that receives deep-breathing training. An ANOVA allows you to compare all of the levels at once to see if, in general, the type of treatment influences how long it takes to fall sleep. If results are statistically significant, post hoc analyses then provide even more information by identifying which specific levels differ from one another, ultimately showing you which specific treatments are more effective than others.

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