There are (at least) two types of scientists: scientist-scientists and science-doers.

Both groups do essential, difficult, demanding, and crucial work that everyone, including the scientist-scientists, needs. The latter group (like the former) includes people who work in research hospitals, water-quality labs, soil-quality labs, linear accelerators, R-&-D labs of all kinds, and thousands of other places. They carry out the daily work of science with precision, care, and a lot of hard work. Yet, at the same time, in the process of doing the doing of science, they typically do not get the luxury of stepping back, moving away from the details, starting over, and discovering the less mechanical, less operational connections among the physical sciences, the social sciences, the humanities, technology, business, mathematics, and statistics… especially the humanities and statistics.

I am not a good scientist, and that has given me the opportunity to step back, start over, do some things right this time, and more importantly, through a series of delightful coincidences, learn more about the meaning of science than about the day-to-day doing of it.[1] This began to happen during my Ph.D., but only some of the components of this experience were due to my Ph.D. studies. The others just happened to be there for me to stumble upon.

The sources of these discoveries took the form of two electrical-engineering professors, three philosophy professors, one music professor, one computer-science professor, some linguistics graduate students, and numerous philosophy, math, pre-med, and other undergrads. All of these people exposed me to ideas, ways of thinking, ways of questioning, and ways of teaching that were new to me.

As a result of their collective influence, my studies, and all my academic jobs from that period, I have come to think of science not merely as the wearing of lab coats and carrying out of mathematically, mechanically, or otherwise challenging complex tasks. I have come to think of science as the following of, for lack of a better expression, the scientific method, although by that I do not necessarily mean the grade-school inductive method with its half-dozen simple steps. I mean all the factors one has to take into account in order to investigate anything rigorously. These include double-blinding (whether clinical or otherwise, to deal with confounding variables, experimenter effects, and other biases), setting up idiot checks in experimental protocols, varying one unknown at a time (or varying all unknowns with a factorial design), not assuming unjustified convenient probability distributions, using the right statistics and statistical tests for the problem and data types at hand, correctly interpreting results, tests, and statistics, not chasing significance, setting up power targets or determining sample sizes in advance, using randomization and blocking in setting up an experiment or the appropriate level of random or stratified sampling in collecting data [See Box, Hunter, and Hunter’s Statistics for Experimenters for easy-to-understand examples.], and the principles of accuracy, objectivity, skepticism, open-mindedness, and critical thinking. The latter set of principles are given on p. 17 and p. 20 of Essentials of Psychology [third edition, Robert A. Baron and Michael J. Kalsher, Needham, MA: Allyn & Bacon, 2002].

These two books, along with Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning and a few other sources that are heavily cited papers on the misuses of Statistics have formed the basis of my view of science. This is why I think science-doing is not necessarily the same thing as being a scientist. In a section called ‘On being a scientist’ in a chapter titled ‘Methodology Wars’, the neuroscientist Fost explains how it’s possible, although not necessarily common, to be on “scientific autopilot” (p. 209) because of the way undergraduate education focuses on science facts and methods[2] over scientific thinking and the way graduate training and faculty life emphasize administration, supervision, managerial oversight, grant-writing, and so on (pp. 208–9). All this leaves a brief graduate or a post-doc period in most careers for deep thinking and direct hands-on design of experiments before the mechanical execution and the overwhelming burdens of administration kick in. I am not writing this to criticize those who do what they have to do to further scientific inquiry but to celebrate those who, in the midst of that, find the mental space to continue to be critical skeptical questioners of methods, research questions, hypothesis, and experimental designs. (And there are many of those. It is just not as automatic as the public seems to think it is, i.e., by getting a degree and putting on a white coat.)

Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, George E. P. Box, William G. Hunter, and J. Stuart Hunter, New York , NY: John Wiley & Sons, Inc., 1978 (0-471-09315-7)

Essentials of Psychology, third edition, Robert A. Baron and Michael J. Kalsher, Needham, MA: Allyn & Bacon, A Pearson Education Company, 2002

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, New York, NY: Springer-Verlag, 2009 (978-0-387-84858-7 and 978-0-387-84857-0)

If Not God, Then What?: Neuroscience, Aesthetics, and the Origins of the Transcendent, Joshua Fost, Clearhead Studios, Inc., 2007 (978-0-6151-6106-8)

[1] Granted, a better path would be the more typical one of working as a science-doer scientist for thirty years, accumulating a visceral set of insights, and moving into the fancier stuff due to an accumulation of experience and wisdom. However, as an educator, I did not have another thirty years to spend working on getting a gut feeling for why it is not such a good idea to (always) rely on a gut feeling. I paid a price, too. I realize I often fail to follow the unwritten rules of social and technical success in research when working on my own research, and I spend more time than I perhaps should on understanding what others have done. Still, I am also glad that I found so much meaning so early on.

[2] In one of my previous academic positions, I was on a very active subcommittee that designed critical-thinking assessments for science, math, and engineering classes with faculty from chemistry, biology, math, and engineering backgrounds. We talked often about the difference between teaching scientific facts and teaching scientific thinking. Among other things, we ended up having the university remove a medical-terminology class from the list of courses that counted as satisfying a science requirement in general studies.