Fallacies – B-C
Commonplace Book – Pages 95-96
Bad Company Fallacy: Guilt by Association is the attempt to discredit an idea based upon disfavoured people or groups associated with it. This is the reverse of an ‘Appeal to Authority,’ and might be justly called ‘Appeal to Anti-Authority.’ An argument to authority argues in favor of an idea based upon associating an authority figure with the idea, whereas Guilt by Association argues against an idea based upon associating it with disruptive people or groups.
Bad Reasons Fallacy: This fallacy consists in arguing that a conclusion is false because an argument given for it is bad. It is most likely to occur in the course of a debate, when one side argues badly for the truth of the preposition, and the other side uses the bad argument as a reason to conclude that the proposition is false.
The Base Rate Fallacy: People who have only generic information tend to use it to judge probabilities, which is the rational thing to do since that’s all they have to go by. In contrast, when people have both types of information, they tend to make judgments of probability based entirely from specific information, leaving out the ‘base rate’ or ‘type I’ information. This is the fallacy.
Biased Sample: A fallacy affecting statistical inferences. Since the strength of statistical inferences depend upon the similarity of the sample of population, they are really a species of argument from analogy, and the strength of the inference varies directly with the strength of the analogy. Thus, a inference will commit this fallacy of the similarity is too weak. Two ways: a) the sample is too small to represent population = subfallacy of Hasty Generalization b) the sample is biased in some way as a result of not having been chosen randomly.
Black or White Fallacy: A validating form of argument. Usually, the truth-value of premises is not a question of logic, or common sense. So, while an argument with a false premise is unsound, it’s usually not considered fallacious. However, when a disjunctive premise is false for specifically logical reasons, or when the support for it is based upon a fallacy, then the argument commits this fallacy.
Card-Stacking: A one-sided case presents only evidence favoring its conclusion, and ignores or downplays the evidence against it. In inductive reasoning, it is important to consider all of the available evidence before coming to a conclusion. However, a defense attorney may present one-sided argument for defense and likewise a prosecutor will present biased evidence for conviction; but together they create a non-one-sided argument.
Fallacy of the Consequent: “If P then Q. Therefore, if Q then P.” One of Aristotle’s 13 fallacies. People commit this fallacy because they think the consequence is convertible. Also, when people base opinions just by sense-perception as Aristotle put it: ‘people think honey is bile, because honey is also yellow.’
Complex Question: ‘Plurium Interrogationum‘ A question with a false, disputed, or question-begging presupposition. For example the question, ‘Have you stopped beating your wife?’ presupposes that you have beaten your wife prior to the asking, as will as that you have a wife. If you are unmarried or have never beaten your wife, then the question is loaded.
Composition: Some properties are such that, if every part of a whole has the property, then the whole till too. However, not all properties are like this, for example atoms. All visible objects are made of atoms, which are too small to see. If P is an expansive property, then the argument form above is validating, but definitions of what such a property is. However, if P is not expansive, then the argument is non-validating, and any argument of that form commits this fallacy.
The Conjunction Fallacy: The probability of a conjunction is never greater than the probability of its conjuncts. In other words, the probability of two things being true can never be greater than the probability of one of them being true, since in order for both to be true, each must be true.
Converse Accident: This is a fallacy of generalizing about a population based upon a sample which is too small to be representative. If the population is heterogeneous, then the sample needs to be large enough to represent the population’s variability. With a completely homogeneous population, a sample of one is sufficiently large, so it’s impossible to put an absolute lower limit on sample size. Rather, sample size depends directly upon the variability of the population: the more heterogeneous a population, the large the sample required.
