Statistical syllogisms...and why generalizations aren’t always accurate.
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Transcript of Statistical syllogisms...and why generalizations aren’t always accurate.
Definitiontype of inductive reasoning based on a probability where the strength of the argument is reliant on the strength of a generalization (major premise)
MAJOR PREMISEgeneralizations which state probabilities that form the basis of succeeding assumptions
The closer the number of the sample to the required number, the more reliable
the generalization is.Ex. Most apples are red.
(If 100 apples exist in the world, the sample must approach 50 in order to be considered reliable.)
Ex. 75% of Asians are shorter than 5’11”.(The statement would be more reliable if the sample included a greater variety of Asians instead of just one nationality.)
The greater the variety of the members of the sample,
the more reliable the generalization is.
Ex. 90% of men like chocolates.(If the number of conflicting cases increases in the sample taken, the generalization is made less reliable.)
The more thorough the search for conflicting cases,
the more reliable the generalization.