The map utterly distorts what is really there and

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The map utterly distorts what is really there and leaves out numerous details about what a particular area looks like. But it is precisely because the map distorts reality – because it abstracts away from a host of details about what is really there – that it is a useful tool. A map that attempted to portray the full details of a particular area would be too cluttered to be useful in finding a particular location or would be too large to be conveniently stored. (Rogers, 2006, p. 276, emphasis in original) The essential point is that models are simplifications. Whether or not they are useful to us depends on what we are trying to accomplish with the particular model. One of the remarkable aspects of models is that they are often more useful to us when they are inaccurate than when they are accurate. The process of thinking about the failure of a model to explain one or more cases can generate a new causal theory. Glaring inaccuracies often point us in the direction of fruitful theoretical progress.
1.5 Rules of the Road to Scientific Knowledge about Politics 17 1.5 RULES OF THE ROAD TO SCIENTIFIC KNOWLEDGE ABOUT POLITICS In the chapters that follow, we will focus on particular tools of political science research. As we do this, try to keep in mind our larger purpose – trying to advance the state of scientific knowledge about politics. As scien- tists, we have a number of basic rules that should never be far from our thinking: Focus on causality. Don’t let data alone drive your theories. Consider only empirical evidence. Avoid normative statements. Pursue both generality and parsimony. 1.5.1 Focus on Causality All of Chapter 3 deals with the issue of causality and, specifically, how we identify causal relationships. When political scientists construct theories, it is critical that they always think in terms of the causal processes that drive the phenomena in which they are interested. For us to develop a better understanding of the political world, we need to think in terms of causes and not mere covariation . The term covariation is used to describe a situation in which two variables vary together (or covary ). If we imagine two variables, A and B , then we would say that A and B covary if it is the case that, when we observe higher values of variable A , we generally also observe higher values of variable B . We would also say that A and B covary if it is the case that, when we observe higher values of variable A , we generally also observe lower values of variable B . 7 It is easy to assume that when we observe covariation we are also observing causality, but it is important not to fall into this trap. (More on this in Chapter 3.) 1.5.2 Don’t Let Data Alone Drive Your Theories This rule of the road is closely linked to the first. A longer way of stating it is “try to develop theories before examining the data on which you will perform your tests.” The importance of this rule is best illustrated by a silly example. Suppose that we are looking at data on the murder rate (number 7 A closely related term is

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