Validation in Simulation: Various Positions in the Philosophy of Science
Management Science
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Inertia and Variety Seeking in a Model of Brand-Purchase Timing
Marketing Science
In Search of Data: An Editorial
Marketing Science
Product Strategy for Innovators in Markets with Network Effects
Marketing Science
Implications of Reduced Search Cost and Free Riding in E-Commerce
Marketing Science
Generalized Robust Conjoint Estimation
Marketing Science
Customized Products: A Competitive Analysis
Marketing Science
Research NoteThe Benefits of Personalized Pricing in a Channel
Marketing Science
Editorial: Fifty Years of Marketing Science
Marketing Science
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One research function is proposing new scientific theories; another is testing the falsifiable predictions of those theories. Eventually, sufficient observations reveal valid predictions. For the impatient, behold statistical methods, which attribute inconsistent predictions to either faulty data (e.g., measurement error) or faulty theories. Testing theories, however, differs from estimating unknown parameters in known relationships. When testing theories, it is sufficiently dangerous to cure inconsistencies by adding observed explanatory variables (i.e., beyond the theory), let alone unobserved explanatory variables. Adding ad hoc explanatory variables mimics experimental controls when experiments are impractical. Assuming unobservable variables is different, partly because realizations of unobserved variables are unavailable for validating estimates. When different statistical assumptions about error produce dramatically different conclusions, we should doubt the theory, the data, or both. Theory tests should be insensitive to assumptions about error, particularly adjustments for error from unobserved variables. These adjustments can fallaciously inflate support for wrong theories, partly by implicitly under-weighting observations inconsistent with the theory. Inconsistent estimates often convey an important message---the data are inconsistent with the theory! Although adjustments for unobserved variables and ex post information are extraordinarily useful when estimating known relationships, when testing theories, requiring researchers to make these adjustments is inappropriate.