“It is inevitable that, in seeking for its greatest unification, science will make itself an object of scientific investigation.” [Morris, 1938]
This book was originally intended as ‘How to do science’, or ‘How to be a scientist’, providing guidance for the new scientist, as well as some reminders and tips for experienced researchers. Such a book does not need to be written by the most expert or most famous scientist, but by one who likes to see the rules of play laid out concisely. It does need to be written by a working scientist, not by a philosopher of science. The first half of the book, called ‘Scientist’s Toolbox’, retains this original focus on what Jerome Brumer called the structure of science -- its methodologies and logic.
This objective is still present in the second half of the book, ‘Living Science’. In researching that section, however, I was fascinated by the perspectives of fellow scientists on ‘What it is like to be a scientist.’ Encountering their insights into the humanity of science, I found resonance with my already intense enjoyment of the process of science. Gaither and Cavazon-Gaither [2000] provide many additional scientific quotations on the experience of science.
Consider the process of science.
Knowledge is the goal of science: basic research seeks reliable knowledge, and applied research seeks useful knowledge. But if knowledge were our primary goal as scientists, we would spend much of our available time in reading the literature rather than in slowly gathering new data. Science is not static knowledge; it is a dynamic process of exploring the world and seeking to obtain a trustworthy understanding of it. Everyone practices this process, to some extent. Science is not the opposite of intuition, but a way of employing reality testing to harness intuition effectively and productively.
As we explore the scientific process in this book, we will attempt to answer some of the following questions.
- History: What are the essential elements of scientific method?
- Variables: How can I extract the most information from my data?
- Induction and pattern recognition: If I cannot think of an experiment to solve my problem, how can I transpose the problem into one more amenable to experimental test? How can I enhance my ability to detect patterns? Where is the boundary between correlation and causality?
- Deduction: How large a role does deduction really play in science? What are some of the more frequent deductive fallacies committed unknowingly by ‘logical’ scientists?
- Experimental techniques: What seemingly trivial steps can make the difference between an inconclusive experiment and a diagnostic experiment? What troubleshooting procedures have proven effective in all branches of science?
- Objectivity: How much do expectations influence observations? In what ways is objectivity a myth? How can we achieve objective knowledge, in spite of the inescapable subjectivity of individuals?
- Evaluation of evidence: When I think I am weighing evidence rationally, what unconscious values do I employ? How much leverage does prevailing theory exert in the evaluation of new ideas?
