Inductive reasoning

==== Anecdotal generalization ====
An anecdotal generalization is a type of inductive argument in which a conclusion about a population is inferred using a non-statistical sample.<ref>{{Cite book|last1=Johnson|first1=Dale D.|url=https://books.google.com/books?id=cMOPtcgQfT8C|title=Trivializing Teacher Education: The Accreditation Squeeze|last2=Johnson|first2=Bonnie|last3=Ness|first3=Daniel|last4=Farenga|first4=Stephen J.|publisher=Rowman & Littlefield|year=2005|isbn=9780742535367|pages=182–83}}</ref> In other words, the generalization is based on [[anecdotal evidence]]. For example:

:So far, this year his son's Little League team has won 6 of 10 games.
:Therefore, by season's end, they will have won about 60% of the games.

This inference is less reliable (and thus more likely to commit the fallacy of hasty generalization) than a statistical generalization, first, because the sample events are non-random, and second because it is not reducible to a mathematical expression. Statistically speaking, there is simply no way to know, measure and calculate the circumstances affecting performance that will occur in the future. On a philosophical level, the argument relies on the presupposition that the operation of future events will mirror the past. In other words, it takes for granted a uniformity of nature, an unproven principle that cannot be derived from the empirical data itself. Arguments that tacitly presuppose this uniformity are sometimes called ''Humean'' after the philosopher who was first to subject them to philosophical scrutiny.<ref>Introduction to Logic. Gensler p. 280</ref>