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For example, persons claiming that International Studies is an appropriate major for students seeking careers in the Foreign Service do not base that claim on an examination of every single International Studies major to whom they refer. If they had such access, the question could be answered directly without the need to create an argument. Instead, an advocate may present several examples of International Studies majors who happen to be members of the Foreign Service, and then use those examples to support the claim that International Studies (IS) is an appropriate major for students seeking a career in the Foreign Service. Such an argument by example is illustrated in the following diagram:
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Part of the persuasiveness of an argument by example is dependent on the number of examples provided and on the representativeness of those examples. For instance, a generalization based on very few examples may be just as unreliable as a generalization based on examples that are not representative. Thus, an arguer is well advised to present as many examples as he or she can, given the constraints of time, and to make sure that the examples presented are representative of the population about which he or she wants to generalize.
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Having provided examples to support an argument, a debater can then make the argument even more persuasive by providing a vivid illustration. Illustrations function differently than examples. Although illustrations alone may not speak to the certainty of an argument, they make an argument more vivid. Continuing with the last hypothetical situation, the debater might find a particularly well-known member of the Foreign Service who also was an International Studies major and use that story to make the claim more vivid. As a slightly different example, consider the claim that participation in debate is common for exceptional lawyers. An illustration might be thought of as a brief story to give added weight to the generalization. A debater might support this claim by finding several examples of exceptional lawyers who were also debaters, and then focusing on a specific person who has a background in both law and debate. For instance, a debater might present a brief story about Laurence H. Tribe, who was born in China to Russian-Jewish parents. He was the United States national debate champion in 1960, and has gone on to become the preeminent constitutional law authority in the United States, winning over 35 cases before the United States Supreme Court.
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An illustration may not provide logical support to an argument, because a single example does not provide sufficient support to make a generalization from one example to a general rule.6 An illustration enables the audience to “visualize” the point that the debater is making and, as a result, the claim becomes more vivid. Thus, examples and illustrations are complementary kinds of evidence: Sufficient and representative examples lend logical support to an argument and illustrations make the claim more vivid and persuasive.
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D. Statistics are useful when a debater wants to make a generalization about some group of people or things. As in the case of argument by example, the debater does not have information about every individual in the group. A statistic starts with a description of a sample of the group, which allows the arguer to state that the sample probably is like the group as a whole. For instance, if 80% of the members of a debate team at a particular university are English majors, the inference could be drawn (rightly or wrongly) that 80% of all collegiate debaters are English majors. In some cases, a statistic can be a persuasive form of factual evidence. Statistics, used properly, are especially powerful evidence in the hands of a skilled debater.
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Any form of evidence can be misleading, and statistics are no exception. Debaters need to be wary about statistics and examine them closely to make sure that the claim supported by the statistics is a good one. For instance, statistics show that women taking hormone replacement therapy have a lower-than-average incidence of coronary heart disease. One interpretation of this statistic is that hormone replacement therapy protects women against coronary heart disease.
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However, a closer look at the data on which the statistic is based also shows that women who received hormone replacement theory were more likely to be from higher socio-economic groups with better-than-average diet and exercise regimens. Thus, the statistic supporting a relationship between hormone replacement therapy and coronary heart disease may be confounded by socioeconomic status of the women in the statistical sample (Lawlor, Smith, and Ebrahim 2004: 464-467). This is just one example where statistics can be misinterpreted. The main point is that statistics do not interpret themselves. People interpret statistics and need to be careful to provide accurate and complete interpretations.
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E. Descriptions of empirical studies generally include statistics associated with a number of variables. Empirical studies are sometimes more persuasive than “raw statistics” because they are based on underlying theoretical explanations as well as on figures.
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For instance, Shijie Yang collected statistics about the effect of hukou reforms on the income gap between rural and urban workers. Collecting data in five provinces between 1999 and 2005, Shijie Yang found that the hukou reforms in those provinces had the opposite of the intended effect. The study concluded that the hukou reforms “actually caused the income gap between urban and rural citizens to become wider, instead of narrower.”
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That study gathered statistics about two variables: hukou reform, and the urban and rural income gap. The statistics were gathered systematically about each of the two variables and then were interpreted in a way that allows the researcher (and consumers of the research) to make a further statement about the relationship of the two variables. Well-conducted empirical studies are persuasive because of the systematic way evidence is gathered and interpreted. In the previous example, the authors began with an underlying theoretical position that hukou reforms would decrease the income gap. The authors then systematically gathered evidence related to both variables and finally interpreted the statistical evidence as inconsistent with the theoretical position with which the study began. In this particular case, the statistician began with theoretical position, and the data gathered actually cast doubt on the original position. Like statistics, different people can interpret empirical studies in different ways. Debaters need to take care to cautiously and accurately interpret empirical studies.
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Thus, observed data, examples and illustrations, historical descriptions, statistics, and descriptions of empirical studies are included in the category called “facts.” Factual evidence of this kind, used well, can be quite persuasive in debate. Sometimes, a collection of facts is gathered together into a complex but coherent interpretation—a theory. The next category of evidence examines that idea of theory.
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16.1.1.2 Theories Theories are used to explain or predict and, thus, can be used as evidence in various cases. In scientific circles, theories are more important than “mere” facts. These theories are formalized statements seeking to predict physical and social phenomena with greater or lesser precision depending on the theory. For instance, formal theories like Albert Einstein’s general and specific theories of relativity or Charles Darwin’s theory of evolution make predictions and explain phenomena.
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But theories need not be that formal to be useful as evidence in argumentation and debate. For instance, debaters might use Samuel Peltzman’s theory of risk compensation to argue against the introduction of kinds of safety devices on automobiles. Peltzman’s theory, which grew out of a study in the mid-1970s about automobile regulation, has since become a much more general theory about risk compensation (Peltzman 1975: 677-726). In simple form, his theory asserts that, when governments issue safety regulations on things from automobiles to motorcycles to birth control devices, people who use those items engage in more risky behavior due to their perception that the safety concerns have been resolved.
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Thus, the Peltzman theory could be used as evidence to argue against instituting more stringent seatbelt laws in China. The argument might go like this: Seatbelt laws will make drivers feel safer; that feeling of safety will cause drivers to drive more recklessly, thus endangering pedestrians and cyclists. The following diagram provides a visual illustration of such an argument:
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One of the reasons that theories are a persuasive category of evidence is that they offer apparently rational explanations for the relationships between and among facts. Contrary to popular opinion, facts do not speak for themselves. In the example presented above, someone might have noticed an increase in pedestrian deaths following the introduction of seatbelt laws, but might not be able to explain why the two phenomena were related. The theory provides just such an explanation. Furthermore, that explanation can then be generalized to other arenas that involve risk. So, a debater might use the theory to argue about related phenomena, such as sports helmets and speed limits.
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Theories are important as evidence because they go beyond “mere” facts and provide seemingly sensible interpretations of the importance and meaning of the facts. Even explanations that are not formal theories are frequently necessary complements to factual evidence.
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16.1.1.3 Presumptions Presumptions are a kind of evidence that does not necessarily describe reality, but describes how people expect reality to be. As such, presumptions are based on what people expect to happen in the ordinary course of events. Presumptions are based on facts, even though they are not facts themselves.
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Presumptions can include assumptions about the nature of people in general, or about specific persons. They also can be about events expected to occur or not occur. For instance, we presume that, next winter, the weather in Guangxi will be warmer than the weather in Harbin. That presumption is not an observable fact because we cannot observe next winter’s weather today. However, the weather in Guangxi has been warmer for so many winters that we presume it will, again, be warmer next winter. We can use that presumption as evidence for a number of arguments, such as, where the family might vacation next December.
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Sometimes, we make presumptions about a particular person based on our previous knowledge of that person or that person’s family characteristics. For instance, someone might argue that Wang Jingkai will become a public servant in China because many of his family members have done so. In this case, the presumption that a particular person will go into public service is based on a fact that other members of his family did just that.
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The laws of many nations contain a concept called a “rebuttable presumption.” Those presumptions are “rebuttable” because a legal system has declared that the presumption stands until other evidence overcomes it. In the area of adoption law, for example, a rebuttable presumption is used to presume that, if a woman is married when she gives birth to a child, her husband is the father. Thus, when one sees a child accompanied by a married woman and her husband, that person might presume that the wife is the mother and the husband is the father. Although one can think of numerous reasons why the presumption might be incorrect, it is a presumption, nevertheless.
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思辨精英：英语辩论-构筑全球视角 [:1705132481]
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16.1.2 Evidence Based on Preference
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