Einstein's Razor: The Principle of Making Scientific Models as Simple as Possible but Not Simpler Is Difficult to Do

Beginning of article

ALBERT EINSTEIN SAID, "EVERYTHING SHOULD BE made as simple as possible, but not simpler." Whether in the physical, biological, or social sciences, this admonition applies, from the hypotheses generated to the explanatory models proposed. Over-simplification could omit one or more important causal variables, or might not adequately capture the interactions among the variables. Overly simplistic models will arouse doubts and fuel skepticism--with justification. Upon reading the article by Frank Borzellieri in SKEPTIC ("Roswell, Aliens, and Belief: Who Believes that Aliens Landed at Roswell?" Vol. 16, No. 4, 21-28), I realized that I was not fully convinced by the explanation of why survey respondents tended (or not) to believe in an alien visitation at Roswell, NM. His conclusions may well be correct, but they are based on a model that may be unnecessarily simplistic.

Although Borzellieri captured many variables in his survey, his conclusions devolved from a set of isolated bivariate analyses. That approach alone is sometimes problematic in social science research. But I also had suspicions that not all relevant variables had been captured in the survey. Furthermore, the survey data were self-volunteered. Such data may have biases that arise because the respondents hold very strong opinions, which they are predisposed to "share" whenever given the opportunity. Conversely, others may not want to respond to a survey, fearing ridicule for their opinion. Less committed individuals may not care to get involved at all in a study that holds little interest for them. Still others may never have heard of Roswell, yet feel compelled to express an opinion.

These potential problems and biases are, in fact, common in scientific research, and I have spent a career implementing methods to deal with them. Thus, Borzellieri's study provides an excellent opportunity to demonstrate how to design and analyze a multivariate study in a complete, transparent, and rigorous multivariate framework. The technology that enables these improvements is the Bayesian Belief Network (BBN). BBNs provide a computational, probabilistic framework for modeling causal relationships among any reasonable …