Editing Inductive reasoning (section)

==== Statistical generalization ====
A statistical generalization is a type of inductive argument in which a conclusion about a population is inferred using a [[Sample (statistics)|statistically representative sample]]. For example:

:Of a sizeable random sample of voters surveyed, 66% support Measure Z.
:Therefore, approximately 66% of voters support Measure Z.

The measure is highly reliable within a well-defined margin of error provided that the selection process was genuinely random and that the numbers of items in the sample having the properties considered are large. It is readily quantifiable. Compare the preceding argument with the following. "Six of the ten people in my book club are Libertarians. Therefore, about 60% of people are Libertarians." The argument is weak because the sample is non-random and the sample size is very small.

Statistical generalizations are also called ''statistical projections''<ref>Schaum's Outlines, Logic, Second Edition. John Nolt, Dennis Rohatyn, Archille Varzi. McGraw-Hill, 1998. p. 223</ref> and ''sample projections''.<ref>Schaum's Outlines, Logic, p. 230</ref>