# Looking for help identifing outliers in a pilot study to guide future hypothesis testing

I am looking to study a particular type of error on a cognitive test to evaluate for potential clinical implications. As there is no existing research on this variable, I would like to run a pilot evaluation on a relatively large pool of subject data from our clinic (feasibly this would be about 200 subjects) to see if there are any trends to guide hypothesis testing in future studies (e.g., are patients from a particular clinical population more likely to make this type of error than those from another population). The data is such that a patient could potentially make 0-15 of these errors, although it's likely that there will be a strong tendency toward the 0-5 range.

Essentially, I am looking to identify subjects from within this pool who are making a relatively higher rate of these errors. Is there a recognized format for doing this type of pilot work? Barring that, what would your recommendations be?

This is obviously very exploratory, so I feel like I would have some latitude in defining what constitutes an 'outlier' for the purposes of this study, but any references or suggestions would be much appreciated.

From my understanding of your problem, the data seems skewed and univariate and the aim explanatory. The first step is plot skewness adjusted box-plots. I know of a $\verb+R+$ implementation in package $\verb+robustbase+$ (look for a function called $\verb+adjbox()+$. The associated white paper is very readable too.