ABSTRACT:
This chapter advocates changes in our theorizing and our testing of theories. These changes would help us to formulate more meaningful theories and to evaluate theories more rigorously. Although the issues and prescriptions apply generally, the discussion emphasizes time series because these are central in studies of evolutionary dynamics. A time series is a sequence of observations collected over time—for example, annual counts of steel mills over 30 years.
This first section explains why time series so readily support multiple interpretations, including spurious or deceptive inferences. This ambiguity implies that we should use tough criteria to test theories about series. This section also points out that sustaining a null hypothesis is often more useful than rejecting one, but journals favor studies that do the opposite and they encourage scientists to lie. The subsequent section reviews six reasons organizational scientists should pay serious attention to null or naïve hypotheses that describe behaviors as having large random components. We are trying too hard to invent and show the superiority of causal theories, often complex ones; and we too quickly reject simple hypotheses that are more parsimonious. The third section points out how often social scientists test their theories against null hypotheses that they can always reject. Statistical tests would have more import if scientists would test their theories against null models or against naïve hypotheses.
