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©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Analyzing and Evaluating Inductive Arguments The aim of this tutorial is to help you learn.

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Presentation on theme: "©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Analyzing and Evaluating Inductive Arguments The aim of this tutorial is to help you learn."— Presentation transcript:

1 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Analyzing and Evaluating Inductive Arguments The aim of this tutorial is to help you learn to analyze and evaluate analogies. Go To Next Slide 10-1

2 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Like all other arguments, inductive arguments try to prove a conclusion by offering premises which, if true, support the truth of the conclusion. The key difference between inductive and deductive arguments is that true premises in a strong inductive argument offer a high probability that the conclusion is true, while true premises in a valid deductive argument guarantee the truth of the conclusion. Thus, evaluating inductive arguments depends on assessing this probability. Go To Next Slide 10-2

3 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide Before we can evaluate an argument, we need to analyze it. We need to be clear about what the argument is trying to prove, what evidence it uses, and how it relates this evidence to its conclusion. Analogies are arguments that deal with comparing two similar things, one which is familiar and one which is unfamiliar. The key to analyzing analogies is to determine what these two things are and how they are similar. 10-3

4 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide An analogy is an argument that works by demonstrating known similarities between two things and then arguing that further unknown similarities exist. Premise 1: X has properties a, b, and c. Premise 2: Y has properties a, b, and c. Premise 3: X has additional property p. Conclusion: Y also has property p. The first two premises demonstrate how X (the sample) and Y (the target) are similar. The third premise demonstrates the property in question. The conclusion states that Y (the target) also has the property in question. 10-4

5 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide So, analyzing an analogy means translating it into standard form. “Three of my friends bought their computers on the internet and they were all unhappy with them. I was thinking about ordering my new computer online but now I think that if I do, I’ll be unhappy with it.” Such translations are made easier since you know from the standard form what parts you need to look for: the sample and the target, the similarities that are known, and the property in question “X.” Remember, the conclusion is always about the target and always asserts that the target has the property in question “X.” 10-5

6 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide “Three of my friends bought their computers on the internet and they were all unhappy with them. I was thinking about ordering my new computer online but now I think that if I do, I’ll be unhappy with it.” p1: Friends’ computers were: (1) bought online p2: My computer will be: (1) bought online p3: Friends’ computers: (2) made them unhappy c: My computer will: (2) make me unhappy 10-6

7 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide p1: Friends’ computers were: (1) bought online p2: My computer will be: (1) bought online p3: Friends’ computers: (2) made them unhappy c: My computer will: (2) make me unhappy Once properly analyzed, we can evaluate this argument. There are several things to consider when evaluating an analogy, but they all boil down to this basic rule, “the more similar the sample and the population, the higher the probability that the conclusion is true.” Each of the individual things to consider in your evaluation is concerned in some way with measuring this similarity. 10-7

8 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide 1. The larger the sample, the stronger the argument. If our computer buyer had 10 friends who were unhappy with the computers they purchased on the internet, the argument would be stronger. 2. The greater the percentage of the sample that has the property in question, the greater the chance that the target has the additional attribute. Consider an analogy with a sample of 10 people who bought computers online. Suppose 3 of the 10 were unhappy. Now suppose that 8 of the 10 were unhappy. Which would make our analogy stronger? 10-8

9 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide 1. The greater the number of relevant similarities between the sample and the target, the stronger the conclusion. If all of our friends’ computers were the same brand as the one we are buying, the analogy gets stronger. If they all ordered from the same company that we are going to use, the analogy gets stronger. 2. The fewer know dissimilarities between the sample and the target, the stronger the argument. If all the friends bought one type of computer and you are considering a different type, the analogy get weaker. If theirs were refurbished and yours will be new, the analogy gets weaker. 10-9

10 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide 1. When considering a feature of the sample that we are unsure of in the target, the greater the diversity in the sample, the better the argument. Consider processor speed. If we don’t know the speed of the processor in the target, we want a large diversity of processors in our sample. This gives a greater likelihood that the sample will be similar to our target population. 2. The more guarded the conclusion is, the stronger the argument is. (Consider the burden of proof!) Consider these two conclusions: (1) I will be terribly unhappy with my computer, (2) I will be less than perfectly pleased with my computer. Conclusion 2 is more guarded. It is much more likely that you’ll be less than pleased than it is that you’ll be terribly unhappy, thus 2 is easier to prove. 10-10

11 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide Unlike determining the validity or invalidity of a deductive argument, evaluating an inductive argument like an analogy is somewhat subjective. The strength or weakness of an analogy will depend, in part, to how relevant and similar the two analogues seem to the reader. Instead of immediately trying to determine in some objective way an analogy’s absolute strength, it is prudent to evaluate it by determining what would make it stronger or by comparing it to other analogies. By comparing the relative strength of an analogy with actual or potential rivals you can get a good sense of its absolute strength. 10-11

12 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide You have to speak Sanskrit before you can say you know it. Would you believe someone who claimed to know French or Spanish and never spoke it? Sanskrit is a language just as they are. p1. French and Spanish are: (1) languages. p2. Sanskrit is: (1) a language. p3. French and Spanish: (2) needs to be spoken to prove fluency. c. Sanskrit: (2) needs to be spoken to prove fluency. How large is the sample? Is the sample diverse? How many similarities are there? Are they relevant? How many dissimilarities are there?How precise is the conclusion? 10-12

13 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide The sample only contains two languages out of hundreds. The analogy would have been stronger if more languages were used. The sample is not very diverse. French and Spanish are both modern languages spoken in Europe. The similarity between the sample and target, that they are all languages is relevant, but more similarities would be better. There is a dissimilarity that is not mentioned. Sanskrit is a dead language, no longer spoken while French and Spanish are modern spoken languages. This is a rather weak analogy. If it had included other ‘dead’ languages, such as Latin or Ancient Greek, it would have been significantly stronger. 10-13

14 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide I’m painting our rooms green. When you go out into a field your eyes feel relaxed; that shows your eyes will feel relaxed in a green room, too. p1. A field is: (1) a green colored environment. p2. A room painted green is: (1) a green colored environment. p3. A field: (2) is relaxing on the eyes. c. A room painted green will be (2) relaxing on the eyes. How large is the sample? Is the sample diverse? How many similarities are there? Are they relevant? How many dissimilarities are there?How precise is the conclusion? 10-14

15 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Go To Next Slide The sample only contains one thing; a green field. The analogy would have been stronger if more green things were used in the sample. The sample is not very diverse; in fact it is not diverse at all. The similarity between the sample and target, that they are both green things, may or may not be relevant to the feeling of relaxation. There are many dissimilarities that are not mentioned. Think of all the differences between a field and a room: one is outside, one inside, one has fresh air, one stale air, one natural light, one artificial, etc. This is a rather weak analogy. The green field and the green room are just not that similar. 10-15

16 ©2007 by The McGraw-Hill Companies, Inc. All rights reserved. From these simple examples you can see the process of analyzing and evaluating analogies. If you are diligent about translating your problems into standard form and applying the evaluation guidelines you’ll find success with analogies. Chapter 11 also contains arguments called inductive generalizations. Though a bit different, the strategies used for analogies will work here as well. Remember, no inductive argument is perfect. If you demand perfection or near perfection in them you’ll never find a strong one. You should be critical but realistic when evaluating inductive arguments. This is the end of the tutorial. 10-16


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