Statistical
syllogisms
...and why
generalizations aren’t
always accurate
What is a statisical
syllogism?
Definition
type of inductive reasoning
based on a probability where
the strength of the argument
is reliant on the strength of a
generalization (major
premise)
WHAT COMPOSES
a Statistical
Syllogism?
MAJOR PREMISE
generalizations which
state probabilities that
form the basis of
succeeding
assumptions
Minor Premise
statement that links
the subject/s of the
conclusion with the
major premise
CONCLUSION
The assumption made
based on the major
premise.
Major Premise
82.5% of IMed
students are from
PSHS.
Minor premise
Jon is an IMed
student.
Conclusion
Jon is a most
probably a graduate
of PSHS.
Major Premise
17.5% of IMed
students are
members of the
Med. Choir.
Minor Premise
Flo is an IMed
student.
Conclusion
It is very likely that
Flo is not a member
of the Med. Choir.
Evaluating the
strength of this
type of argument
is a matter of
degree.
The reliability of
the argument
must be
evaluated using
three questions.
Are there enough
cases to support a
universal statement
or one that is merely
general?
Have the observed
cases been found in
every variety of
times, places and
circumstances?
Has a thorough
search been made
for conflicting cases?
criteria for evaluating
the strength of a
generalization
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.)
The greater the variety of
the members of the sample,
the more reliable the
generalization is.
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 more thorough the
search for conflicting cases,
the more reliable the
generalization.
Ex. 90% of men like chocolates.
(If the number of conflicting cases increases in
the sample taken, the generalization is made
less reliable.)
Fallacies
involving
statistical
syllogism
accident
application of a general
rule when circumstances
suggest an exception.
Converse accident
application of an
exception to the rule
when the generalization
should apply.