Download
1 / 7
80 likes | 180 Views
Inductive Reasoning. Aims at probability, not certainty In conclusion: it is likely that, it is reasonable to conclude that, it is probable that In premises: it is reasonable to think that, it is likely that, generally (look for any generalizations), statistical and/or demographic data.
E N D
Inductive Reasoning • Aims at probability, not certainty • In conclusion: it is likely that, it is reasonable to conclude that, it is probable that • In premises: it is reasonable to think that, it is likely that, generally (look for any generalizations), statistical and/or demographic data
Types of Inductive Arguments • Predictions • Retrodictions (predictions about the past based on current information) • Analogies • Statistical Reasoning • Causal Reasoning / Explanations
Evaluating Inductive Reasoning in General • Relevance: Is the evidence appealed to relevant to the truth of the conclusion? Could it potentially support the conclusion? • Corroboration: How much support do the premises provide for the conclusion? How likely is it, given the evidence, that the conclusion is true?
Evaluating Inductive Arguments in General (continued) • Validity/Invalidity is inappropriate since (1) inductive reasoning cannot in principle supply conclusive reasons, and (2) inductive reasoning does not aim at conclusiveness. • Soundness/Unsoundness can’t be used because it relies on Validity/Invalidity. • Inductive reasoning is evaluated for STRENGTH
Strength • Evaluation of the degree to which the evidence can show the conclusion to be true • Not all-or-nothing like validity and soundness, but rather a matter of degree • Inductive arguments are strong to the degree to which the evidence appealed to (1) is relevant to the truth of the conclusion, and (2) increases the likelihood that the conclusion is true. • How you make the assessment of strength will vary depending on the nature of the argument. Some examples include: for analogies, similarities and dissimilarities; for explanations, simplicity, unification or scope; for statistical reasoning, significance or correlation.
Some Examples • Well, sure Christianity is true. After all, nearly 2 billion people worldwide believe it. • Too many people are abusing the public aid system. Just think about that lady who was on the news last night—her kids were living in filth while she was spending all of her money on booze. • Joe’s accident was caused by weather conditions. It was dark and foggy, and the freezing rain from earlier in the day had left the roads with a thin sheet of ice. • I think Amy would enjoy studying philosophy. She is inquisitive, unafraid to express her opinion, and enjoys reasoned argumentation. Every philosopher I know is like this.
More Examples • IVCC should make a vending deal with Coca-Cola instead of PepsiCo. 90% of students surveyed said they prefer Coke products over Pepsi. • We should ban marijuana because it is a gateway drug. Studies show that nearly everyone who has used “hard” drugs like cocaine and heroin have used marijuana. • Last night, 25 people reported to the ER with symptoms of food poisoning from E. coli. Upon investigation, it was discovered that each of them has eaten at the McDonald’s that day. Since E. coli is generally found in meat stored or handled under unsanitary conditions, we have concluded that the McDonald’s had tainted meat.
