More Thinking About Science

The other day I posted on this blog suggesting that scientists do not necessarily believe that the world is defined by laws and is thus predictable and amenable to mathematical treatment. Rather, I suggested, scientists seek areas that are observably exhibiting patteerns, and seek to explore and describe those patterns.

I want to expand on that discussion, considering how scientists do their work. It occurs to me that we might all adopt some of their approach in our daily problem solving.

My experience is that scientists are generally very hard working. The Post-Doctorate scientists who worked for me over the years tended to work long hours, with great diligence, and to think about their work in their "off hours". They worked much harder on the average than our non-scientist colleagues. Invention is a small part inspiration and a large part perspiration.

Scientists generally are reductionist, selecting a specific question to explore. They are pragmatic, selecting questions that are likely to yield to their efforts in a reasonable time with the resources that they are able to bring to bear on that problem.

On the other hand, many scientists synthesize scientific results, creating text books, teaching overview courses, or writing books to describe new syntheses.

Scientists are cautious about their observations, recognizing that errors often occur in the process. They depend on independent replication of results, and accept that their observations may not be valid.

Scientists are also caution about the interpretation of their observations. While science is about understanding causal explanations of that which is observed, scientists are generally reluctant to extrapolate much beyond those observations (at least in public), and recognize that their extrapolations may prove erroneous.

They work within paradigms, which define the important problems of the moment, and the approaches to those problems, recognizing that paradigm shifts may occur.

Scientists are very well informed about the paradigm in which they are working and related paradigms. They read the literature, attend scientific meetings, and participate in scientific exchanges after a formal education that almost always includes graduate degrees, usually a doctorate, and sometimes more than one.

They tend to be very collaborative. While most work in groups, all subject their work to peer review. It is only through the social construction of information that scientific knowledge is accepted.

Scientists are theory driven, seeking to make observations that extend or clarify accepted theories.

They are especially interested in exceptions, observations that do not correspond to the predictions of currently accepted theories.

Scientists recognize that the most important advances often come from looking at old problems in new ways, Often progress is made by applying a method or approach from one field in a new field. Thus good scientists are interested in cross-disciplinary approaches.

The best scientists are very good at selecting really interesting questions. They allocate their problem solving resources to questions that they can not only make progress on, but for which that progress will significantly illuminate larger issues and questions. That choice is based on intuition, but it appears to be informed intuition. The fact that most of the best scientists were students of others of the best scientists suggests that there is tacit understanding of the ways to choose good questions on which to work which can be transmitted through apprenticeship.

Scientists think about methods. Their metathinking guides their action.

One of the things that scientists have difficulty with, as do we all, is extrapolating what they understand about how to do their work in their chosen field to obtain lessons on how to work in other fields.

I worked with many scientists who were leaving the research laboratory to work in international development. It took some mentoring to convince them that they should master the literature in international development even though they would not think of taking on a new scientific project without mastering the relevant scientific literature, It also took some mentoring to suggest that the same skills that they used in judging the credibility of information could be applied to project monitoring and evaluation, or to development statistics.