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In this day and age of extraordinary computing power, the likes of which will probably seem laughable in even a decade from the date of publication of this book, with a few clicks of the mouse and a software manual, one can obtain a principal components analysis, factor analysis, discriminant analysis, multiple regression, and a host of other relatively theoretically advanced statistical techniques in a matter of seconds. The advance of computers and especially easy‐to‐use software programs has made performing statistical analyses seemingly quite easy because even a novice can obtain output from a statistical procedure relatively quickly. One consequence of this however is that there seems to have arisen a misunderstanding in some circles that “applied statistics” somehow equates with the idea of “statistics without mathematics” or even worse, “statistics via software.”

The word “applied” in applied statistics should not be understood to necessarily imply the use of computers. What “applied” should mean is that the focus on the writing is on how to use statistics in the context of scientific investigation, oftentimes with demonstrations with real or hypothetical data. Whether that data is analyzed “by hand” or through the use of software does not make one approach more applied than the other. If analyzed via computer, what it does make it is more computational compared to the by‐hand approach. Indeed, there is a whole field of study known as computational statistics that features a variety of software approaches to data analysis. For examples, see Dalgaard (2008), Venables and Ripley (2002), and Friendly (1991, 2000) for an emphasis on data visualization. Fox (2002) also provides good coverage of functions in S‐Plus and R. And of course, computer science and the machine‐learning movement have contributed greatly to software development and our ability to analyze data quickly and efficiently via algorithms, and implement new and classic procedures that would be impossible otherwise.

On the opposite end of the spectrum, if a course in statistics is advertised as not being applied, then most often what this implies is that the course is more theoretical or mathematical in nature with a focus on proof and the justification of results. In essence, what this really means is that the course is usually more abstract than what would be expected in an applied course. In such theoretical courses, very seldom will one see applications to real data, and instead the course will feature proofs of essential statistical theorems and the justification of analytical propositions. Hence, this is the true distinction between applied versus theoretical courses. The computer has really nothing to do with the distinction other than facilitating computation in either field.