Experimental design heuristics for scientific discovery: the use of "baseline" and "known standard" controls

Abstract What type of heuristics do scientists use when they design experiments? In this paper, we analysed the ways biological scientists designed complex experiments at their weekly laboratory meetings. In two studies, we found that one of the key components of experimental design is that specific types of control conditions are used in the service of different goals that are important in scientific discovery. “Baseline” control conditions are identical to the experimental manipulation, except that a key feature, such as a reagent, is absent from the control condition and present in the experimental condition. “Known standard” control conditions involve performing the experimental technique on materials where the expected result is already well known; if the expected result is obtained, the scientist can have confidence that the procedure is working. In Study 1, which analysed transcripts of real-world biology laboratory meetings, we found that scientists used baseline controls when testing hypotheses and known standard controls when focusing on possible error. In Study 2, undergraduate science students were asked to address the goals of hypothesis testing and dealing with potential error as they designed experiments. Like the real-world scientists, science majors proposed baseline controls to test hypotheses and known standard controls to deal with potential error. We argue that baseline control conditions play an important role in hypothesis testing: unexpected results obtained on baseline control conditions can alert scientists that their hypotheses are incorrect, and hence should encourage the scientists to reformulate their hypotheses. We further argue that use of known standard controls is a heuristic that enables scientists to solve an important problem in real-world science: when to trust their data. Both of these heuristics can be incorporated into experimental design programs, thus making it more likely that scientific discoveries will be made.

[1]  Kevin Dunbar,et al.  Constraints on the experimental design process in real-world science , 1996 .

[2]  Jyotsna Vaid,et al.  Creative Thought: An Investigation of Conceptual Structures and Processes , 2001 .

[3]  P. Wason On the Failure to Eliminate Hypotheses in a Conceptual Task , 1960 .

[4]  P Langley,et al.  Proceedings of the 19th Annual Conference of the Cognitive Science Society , 1997 .

[5]  H. Legrand,et al.  Experimental inquiries : historical, philosophical and social studies of experimentation in science , 1990 .

[6]  Frederick Grinnell,et al.  The Scientific Attitude , 1987 .

[7]  Raúl E. Valdés-Pérez,et al.  Computer science research on scientific discovery , 1996, The Knowledge Engineering Review.

[8]  Padraic Monaghan,et al.  Proceedings of the 23rd annual conference of the cognitive science society , 2001 .

[9]  J. C. Gibbings,et al.  The Systematic experiment : a guide for engineers and industrial scientists , 1986 .

[11]  Lorenzo Magnani,et al.  Model-Based Reasoning in Scientific Discovery , 1999, Springer US.

[12]  Philip Smith Roger Bakeman John M. Gottman , 1987, Animal Behaviour.

[13]  K. Dunbar How scientists think: On-line creativity and conceptual change in science. , 1997 .

[14]  K. Dunbar HOW SCIENTISTS REALLY REASON: SCIENTIFIC REASONING IN REAL-WORLD LABORATORIES , 1995 .

[15]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[16]  John R. Anderson,et al.  The generality/specificity of expertise in scientific reasoning , 1999, Cogn. Sci..

[17]  D. Klahr,et al.  When to trust the data: Further investigations of system error in a scientific reasoning task , 1996, Memory & cognition.

[18]  Martin Stacey,et al.  Scientific Discovery: Computational Explorations of the Creative Processes , 1988 .

[19]  David Klahr,et al.  Dual Space Search During Scientific Reasoning , 1988, Cogn. Sci..

[20]  Jan Maarten Schraagen,et al.  How Experts Solve a Novel Problem in Experimental Design , 1993, Cogn. Sci..

[21]  K. Dunbar The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory. , 2001 .

[22]  Raúl E. Valdés-Pérez,et al.  Conjecturing Hidden Entities by Means of Simplicity and Conservation Laws: Machine Discovery in Chemistry , 1994, Artif. Intell..

[23]  Herbert A. Simon,et al.  Scientific discovery: compulalional explorations of the creative process , 1987 .

[24]  Michael E. Gorman,et al.  How the possibility of error affects falsification on a task that models scientific problem solving , 1986 .

[25]  Clifford R. Mynatt,et al.  Confirmation Bias in a Simulated Research Environment: An Experimental Study of Scientific Inference , 1977 .

[26]  Michael E. Gorman,et al.  Error, Falsification and Scientific Inference: An Experimental Investigation , 1989 .

[27]  D L Brutlag,et al.  Genomics and computational molecular biology. , 1998, Current opinion in microbiology.

[28]  David Turnbull,et al.  Manipulable Systems and Laboratory Strategies in a Biomedical Institute , 1990 .