 W e show that social scientists often do not take full advantage of the information available in their statistical results and thus miss opportunities to present quantities that could shed the greatest light on their research questions. In this article we suggest an approach, built on the technique of statistical simulation, to extract the currently overlooked information and present it in a reader-friendly manner. More specifically, we show how to convert the raw results of any statistical procedure into expressions that (1) convey numerically precise estimates of the quantities of greatest substantive interest, (2) include reasonable measures of uncertainty about those estimates, and (3) require little specialized knowledge to understand.The following simple statement satisfies our criteria: "Other things being equal, an additional year of education would increase your annual income by $1,500 on average, plus or minus about $500." Any smart high school student would understand that sentence, no matter how sophisticated the statistical model and powerful the computers used to produce it. The sentence is substantively informative because it conveys a key quantity of interest in terms the reader wants to know. At the same time, the sentence indicates how uncertain the researcher is about the estimated quantity of interest. Inferences are never certain, so any honest presentation of statistical results must include some qualifier, such as "plus or minus $500" in the present example. Our computer program, "CLARIFY: Software for Interpreting and Presenting Statistical Results," designed to implement the methods described in this article, is available at http://GKing.Harvard.Edu, and won the Okidata Best Research Software Award for 1999. We thank Bruce Bueno de Mesquita, Jorge Domínguez, Geoff Garrett, Jay

, website http://GKing.Harvard.Edu. Clarify is copyrighted, but you may copy and distribute this program provided that no charge is made and the copy is identical to the original. To request an exception, please contact Michael Tomz.

We show that social scientists often do not take full advantage of the information available in their statistical results and thus miss opportunities to present quantities that could shed the greatest light on their research questions. In this article we suggest an approach, built on the technique of statistical simulation, to extract the currently overlooked information and present it in a reader-friendly manner. More specifically, we show how to convert the raw results of any statistical procedure into expressions that (1) convey numerically precise estimates of the quantities of greatest substantive interest, (2) include reasonable measures of uncertainty about those estimates, and (3) require little specialized knowledge to understand.The following simple statement satisfies our criteria: "Other things being equal, an additional year of education would increase your annual income by $1,500 on average, plus or minus about $500." Any smart high school student would understand that sentence, no matter how sophisticated the statistical model and powerful the computers used to produce it. The sentence is substantively informative because it conveys a key quantity of interest in terms the reader wants to know. At the same time, the sentence indicates how uncertain the researcher is about the estimated quantity of interest. Inferences are never certain, so any honest presentation of statistical results must include some qualifier, such as "plus or minus $500" in the present example.

We show that social scientists often do not take full advantage of the information available in their statistical results and thus miss opportunities to present quantities that could shed the greatest light on their research questions. In this article we suggest an approach, built on the technique of statistical simulation, to extract the currently overlooked information and present it in a reader-friendly manner. More specifically, we show how to convert the raw results of any statistical procedure into expressions that (1) convey numerically precise estimates of the quantities of greatest substantive interest, (2) include reasonable measures of uncertainty about those estimates, and (3) require little specialized knowledge to understand.The following simple statement satisfies our criteria: "Other things being equal, an additional year of education would increase your annual income by $1,500 on average, plus or minus about $500." Any smart high school student would understand that sentence, no matter how sophisticated the statistical model and powerful the computers used to produce it. The sentence is substantively informative because it conveys a key quantity of interest in terms the reader wants to know. At the same time, the sentence indicates how uncertain the researcher is about the estimated quantity of interest. Inferences are never certain, so any honest presentation of statistical results must include some qualifier, such as "plus or minus $500" in the present example.

What is the relative importance of structural versus contextual forces in the birth and death of scientific theories? We describe a formal dynamic model of the birth, evolution, and death of scientific paradigms based on Kuhn's Structure of Scientific Revolutions. The model represents scientific activity as a changing set of coupled institutions; a simulated ecology of interacting paradigms in which the creation of new theories is stochastic and endogenous. The model captures the sociological dynamics of paradigms as they compete against one another for members, solve puzzles, and recognize anomalies. We use sensitivity tests and regression to examine the role of intrinsic versus contextual factors in determining paradigm success. We find that situational factors attending the birth of a paradigm largely determine its probability of rising to dominance, while the intrinsic explanatory power of a paradigm is only weakly related to the likelihood of success. For those paradigms surviving the emergence phase, greater explanatory power is significantly related to longevity. However, the relationship between a paradigm's "strength" and the duration of normal science is also contingent on the competitive environment during the emergence phase. Analysis of the model shows the dynamics of competition and succession among paradigms to be conditioned by many positive feedback loops. These self-reinforcing processes amplify intrinsically unobservable microlevel perturbations in the environment-the local conditions of science, society, and self faced by the creators of a new theory-until they reach macroscopic significance. Such path dependent dynamics are the hallmark of self-organizing evolutionary systems. We consider the implications of these results for the rise and fall of new ideas in contexts outside the natural sciences such as management fads.