Q&A: John Bickle

The fifth edition of

Understanding Scientific Reasoning

, co-authored by philosophy department head John Bickle, was published in July by Thomson Wadsworth. Bickle’s comments on the text and its recent revisions explain why it has been so successful.

Q: The book is actually fascinating reading, but you wrote it as a text. Correct?

A: Correct. For the fifth edition, my primary job was to bring it up-to-date scientifically. Earlier editions have been used widely in undergraduate education, ranging from courses on critical thinking to philosophy of science to science studies to scientific methods. It introduces the full range of scientific reasoning—theoretical, statistical, and causal reasoning, plus rational decision-making—by way of the underlying statistics and detailed case studies from a variety of disciplines.

Q: The work is unique because of the deliberate attempt to make students recognize that the philosophy of science is not esoteric but directly relevant to past and current human experience. Explain how this works in part one where your co-authors focus on theoretical hypotheses.

A: There is widespread misunderstanding of ‘“theory” as that term is used in scientific contexts. People tend to think of “theory” as opposed to “fact,” as when someone says that some hypothesis is “just a theory.” In science, however, a theory is a family of

models

and a set of

theoretical hypoteheses

postulating that specific events in the real world fit one of the family’s models. In the book’s early chapters we provide and explain a neat illustration that relates these features to observation and experimentation, and then illustrate this process using well-known cases from the history of science.

Q: You wrote parts two and three on evaluating statistical and casual hypotheses and knowledge, values, and decision making. Define “casual relation” and give examples of what students will learn.

A: Analyzing “cause” is a difficult endeavor—as the past 2,500 years of philosophy attests. We don’t attempt that. Our project is more “internal” to science. Whatever causes actually are, we explain the statistical relationships between causal hypotheses and models (both deterministic and probabilistic). We explicitly distinguish cause from statistical correlation and present some recent case studies that illustrate and distinguish controlled, prospective, and retrospective experimental designs. I also inserted a new section on the search for causal mechanisms, especially in recent bio-medical science.

Q: You used different exercises from those in earlier editions. Why?

A: I drew many new exercises—brief descriptions of scientific experiments and results—from internet news sources, some as recent as 2005. Examples range from a study of urban hardship levels across major U.S. cities to the effects of “yo-yo” dieting on immune system function to genetic links for more effective cancer drug therapies. We instruct readers to apply the resources we develop and illustrate for evaluating each type of scientific reasoning. One goal of the book is to make people better scientific reasoners—and hence better consumers of scientific information.

Q: In part three you concentrate on models of decision making. What do you most want students to understand after having read this material?

A: I seek to convince readers that scientific reasoning (not just its products) is applicable to daily decision-making. I try to communicate one resource, modern utility theory, that the social sciences have developed for combining probabilities of outcomes with “subjective” judgments of value. Finally, I hope that readers will see ways to “concretize” statistical discussions into easy-to-grasp (but still mathematically legitimate) conceptual models so that real-life decisions (like whether to smoke cigarettes) can be evaluated in a new light.