![t test minitab t test minitab](https://s3.studylib.net/store/data/007129820_1-aa226395b2918a59a0ffa10708447f63-768x994.png)
![t test minitab t test minitab](https://blog.minitab.com/hubfs/Imported_Blog_Media/paired_t_swo.png)
The null hypothesis value is that variances are equal, which produces an F-value of 1. Our F-value of 3.30 indicates that the between-groups variance is 3.3 times the size of the within-group variance. Is ours large enough?Ī tricky thing about F-values is that they are a unitless statistic, which makes them hard to interpret. To be able to conclude that not all group means are equal, we need a large F-value to reject the null hypothesis. Going back to our example output, we can use our F-ratio numerator and denominator to calculate our F-value like this: In this case, it becomes more likely that the observed differences between group means reflect differences at the population level.
![t test minitab t test minitab](https://statistics.laerd.com/minitab-tutorials/img/istt/options-example-independent-t-test-numeric-highlighted.png)
The relevant point is that this number increases as the group means spread further apart. The meaning of this number is not intuitive because it is the sum of the squared distances from the global mean divided by the factor DF. Looking back at the one-way ANOVA output, which statistic do we use for the between-group variance? The value we use is the adjusted mean square for Factor (Adj MS 15.540).
![t test minitab t test minitab](https://image1.slideserve.com/3198333/minitab-ci-and-hypothesis-tests-for-a-single-mean-l.jpg)
The between-group variance increases as the dots spread out. This graph represents each group mean with a dot. The dot plot illustrates how this works by comparing two sets of group means.