Editing Fallacy (section)

=== Faulty generalization ===
A special subclass of the informal fallacies is the set of [[faulty generalization]]s, also known as inductive fallacies. Here, the most important issue concerns inductive strength or methodology (for example, [[statistical inference]]). In the absence of sufficient evidence, drawing conclusions based on induction is [[Warrant (rhetoric)|unwarranted]] and fallacious. With the backing of sufficient amounts of the right type of [[empirical evidence]], however, the conclusions may become warranted and convincing (at which point the arguments are no longer considered fallacious).<ref>{{Cite journal |last=Neyman |first=J. |date=1937-08-30 |title=Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability |journal=Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences |language=en |volume=236 |issue=767 |pages=333–380 |bibcode=1937RSPTA.236..333N |doi=10.1098/rsta.1937.0005 |issn=0080-4614 |s2cid=19584450 |doi-access=free}}</ref>

==== Hasty generalization ====
[[Hasty generalization]] is described as making assumptions about a whole group or range of cases based on a [[Sample (statistics)|sample]] that is inadequate (usually because it is atypical or just too small).
Stereotypes about people ("frat boys are drunkards", "grad students are nerdy", "women don't enjoy sports", etc.) are common examples of the principle.

Hasty generalization often follows a pattern such as:

:X is true for A.
:X is true for B.
:Therefore, X is true for C, D, etc.

While never a valid logical deduction, if such an inference can be made on statistical grounds, it may nonetheless be convincing. This is because with enough empirical evidence, the generalization is no longer a hasty one.