Null hypothesis for anova one way

You can test multiple contrasts simultaneously. I Help to Study Useful information for students. Search Search. Writing a null hypothesis for anova. Share this:. When the p-value is less than the significance level, the usual interpretation is that the results are statistically significant, and you reject H 0.

For one-way ANOVA, you reject the null hypothesis when there is sufficient evidence to conclude that not all of the means are equal. The Method table indicates whether Minitab assumes that the population variances for all groups are equal. Look in the standard deviation StDev column of the one-way ANOVA output to determine whether the standard deviations are approximately equal. In this case, Minitab performs Welch's test, which performs well when the variances are not equal.

No matter which software you use, you will receive the following table as output:. If the p-value is less than your chosen significance level e. Suppose we want to know whether or not three different exam prep programs lead to different mean scores on a certain exam.

To test this, we recruit 30 students to participate in a study and split them into three groups. The students in each group are randomly assigned to use one of the three exam prep programs for the next three weeks to prepare for an exam. At the end of the three weeks, all of the students take the same exam. You would report this as:. Although you know that the means are unequal, one-way ANOVA does not tell you which means are different from which other means.

It would be very nice to know whether the mean in the One Dollar condition was higher than the means of the other two conditions. In ANOVA, testing whether a particular level of the IV is significantly different from another level or levels is called post hoc testing. Hey, that sounds familiar! Go ahead and open post hoc. You should get this:. If you set your alpha level to. That means that if you perform 20 significance tests, each with an alpha level of.

As the number of tests increases, the probability of making a Type I error a false positive, saying that there is an effect when there is no effect increases. The multiple comparison problem is that when you do multiple significance tests, you can expect some of those to be significant just by chance. Fortunately, there is a solution:. First, note that the first word here is "Tukey", as in John Tukey the statistician, not as in the bird traditionally eaten at Thanksgiving.

So, in that dialog for Post Hoc Comparisons, check the box next to "Tukey", then make sure "condition" is in the right hand box like shown. Some new output appears:. Then elaborate on those by presenting the pairwise comparison results and, along the way, insert descriptive statistics information to give the reader the means:. Students commonly use the block of text above as a template for answering the homework problems involving ANOVA.