![]() ![]() The median looks to be at about four points higher on the post-test and the mean is above that. We can see right away that the whole distribution has shifted to the right, and this time it is skewed to the right. Let’s take a look at another box and whisker plot, this time for our experimental group in the same study. This isn’t really unexpected – you wouldn’t really anticipate large improvements in mathematics performance over only eight weeks. We can also see that zero falls squarely in the middle of our 95% confidence interval, so we can accept the null hypothesis that no significant increase in performance on the math test occurred for the control group. The most extreme difference for the control group was an increase from pretest to post-test of 11 points. There are no really extreme outliers, and the distribution is a little skewed to the left, with the mean to the left of the median. you can see that the mean difference between pre- and post-test for the control group was close to zero. The value plotted is the difference between post-test and pretest. It is from the control group in our pilot study of Spirit Lake: The Game. The example below is part of the output from a t-test task in SAS Enterprise Guide. In the t-test output, SAS also shades an area for the 95% confidence interval. This is what SAS does.) If there are any outliers beyond 1.5 times the inter-quartile range, they’ll be shown as asterisks after the end of the whisker. (Different software packages use different values for the whiskers. In other words, each whisker MAY extend up to 1.5 times the length of the box. ![]() The inter-quartile range is the distance from the 25th percentile to the 50th. The whiskers, those two lines at either end, extend from the box as far as the minimum and maximum values, up to 1.5 times the inter-quartile range. ![]() That line in the middle is the median, also known as the 50th percentile. The BOX extends from the 25th percentile to the 75th percentile. Box and whisker plots can give you an understanding of your data at a glance – IF you know what you’re looking at. ![]()
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