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Terms in this set (30)
An argument is valid if and only if it is logically impossible for the conclusion to be false, assuming that all the premises are true
An argument is inductively strong if and only if, assuming all the premises are true, the conclusion could be false, but is still more likely to be true than false
An argument is fallacious if and only if, assuming all the premises are true, the conclusion is not more likely to be true than false
An argument is sound if and only if its strength of reasoning is valid, and all of its premises are acceptable
An argument is cogent if and only if its strength of reading is inductively strong, and all of its premises are acceptable
An argument is bad if and only if its strength of reasoning is fallacious, or any of its premises are unacceptable
The inductive argument from X% (>50%) of F's are G's. A is an F. Therefore, A is a G.
The inductive argument from X% of an observed sample of F's are G's. The observed sample is sufficiently large and unbiased. Therefore, X% of F's are G's.
Fallacy of Small Sample
When someone attempts to make a statistical generalization, but they do not have the premise that their sample is sufficiently large
Fallacy of Sample Bias
When someone attempts to make a statistical generalization, but they do not have the premise that their sample is sufficiently unbiased
Extremely unlikely events are commonplace
Events with a sufficiently small probability never occur
Law of Inevitability
Something must happen.
If you make a complete list of all possible outcomes, then one of them must occur.
The Law of Truly Large Numbers
With a large enough number of opportunities, any outrageous thing is likely to happen
Law of Selection
You can make things as likely as you want if you choose after the event
The mistaken inference that a causal factor is required to explain some pattern, when that pattern really is just a regression to the mean
Fallacy of Pst Hoc, Ergo Propter Hoc
The mistaken inference of inferring that A causes B just because A preceded B, or because A is correlated with B.
Inference to the Best Explanation
The inductively strong argument form: we have observed some phenomenon Q. And E provides the best explanation for Q. Therefore, E is true.
When A and B are correlated, neither because A causes B nor because B causes A, but because some third cause C is the confound that is causing them both
p(A|B) is the probability that A is true, given B
Mistakenly reasoning in a way that uses p(A|B) in a situation where what is needed is p(B|A)
Law of the Probability Lever
A slight change in circumstances can have a huge impact on probabilities
Law of Never Enough
Events which are sufficiently similar are often regarded as identical by our minds
A hypothesis that explains more phenomena is, to that extent, better than a hypothesis that explains less
A hypothesis that makes fewer assumptions is, to that extent, better than a hypothesis that makes more assumptions
A hypothesis that is consistent with what we already take to be true is, to that extent, better than a hypothesis that contradicts what we already take to be true
A hypothesis that makes novel predictions is, to that extent, better than a hypothesis that does not point us towards new discoveries
The mental shortcut that uses how easy it is to think of instances of an A as its heuristic attribute, for the target attribute of how likely A is to occur.
The heuristic that has the target attribute of how likely is something to be a member of some category, and the heuristic attribute of how much does that thing resemble our mental picture of a typical member of that category
Base Rate Neglect
When evaluating the probability of some event happening, we tend to ignore general information about how often that kind of event happens, and just focus on what we take to be specific to the case.
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