“A simple and useful strategy is to perform one’s analysis both robustly and by standard methods and to compare the results. If the differences are minor, either set can be presented. If the differences are not, one must perforce consider why not, and the robust analysis is already at hand to guide the next steps.
The importance of these considerations is enhanced when we are dealing with large amounts of data, since then examining all the data in detail is impractical, and we are forced to contemplate working with data that is, almost certainly, partially bad and with models that are almost certainly inadequate.”
This blog is a companion to my recent book, Exploring Data in Engineering, the Sciences, and Medicine, published by Oxford University Press. The blog expands on topics discussed in the book, and the content is heavily example-based, making extensive use of the open-source statistical software package R.