Section 30:
Induction and Probability
, Barker, pp. 181185.
***Read the section, then the following additional comments.
In this lesson we look at
inductive logic
.
Of course, there will be rules and some
standardization.
But, appearances notwithstanding, we are now in the territory of
informal
logic
.
What this means is that we are dealing with arguments whose conclusions do not flow
from the premises with ironclad necessity.
Any inductive argument begins with
observation
.
Such an observation can be as simple as
opening your eyes and looking or as complex as gathering statistics on a certain subject.
In
either case somehow the initial gathering of information is crucial.
We accumulate certain facts
which we have observed to be true.
An inductive argument concludes by making a statement that in some way goes beyond the
observation.
The conclusion could be a generalization about all similar facts (particular to
general) or it could be a prediction about a yettobeobserved item (particular to particular).
Whatever the case, an inductive argument tells us on the basis of observation what we might find
to be true of things which we have not yet observed.
Consequently, an inductive argument can only deal in
probability
.
But don’t let that word
throw you off.
In many cases the probability of an induced conclusion may be so high as to be
virtually certain and irrefutable, though at other times it could be fairly improbable.
The point is
that, as long as we extend ourselves beyond observations, there is always some chance—no
matter how minute—that the next observation might throw over our conclusion.
In practice, that
possibility is often too improbable to worry about.
For example, after having eaten thousands of
hamburgers in my lifetime, I conclude (inductively) that the next hamburger I eat will nourish
me in the same manner as all the previous ones.
This judgment is a matter of probability, to be
sure, but it’s not a gamble; the probability is virtual certainty.
An inductive argument with a highly probable conclusion is a
strong
argument (corresponding
to a “valid” deductive argument).
Combining a strong argument with true premises produces a
reliable
inductive argument (corresponding to a “sound” deductive argument).
***Prepare for submission Exercises 30 A and B.
Examples
30A
1.
This is a relatively weak statement.
Yes, there is predictive value in weather
observations, but we all know that such observations always have degrees of
1
LESSON 12
Inductive Logic
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Documentprobability, which are sometimes very limited.
You can’t fault yourself for missing
on a weather prediction.
30B
1.
This is, in fact, a silly statement by itself.
Sure, ultimately all statements are either
exactly true or exactly false, but we don’t always know these things with exactness.
What probability and improbability are about is helping us to determine whether a
This is the end of the preview.
Sign up
to
access the rest of the document.
 Spring '11
 BrentKelly
 Logic, Inductive Reasoning, Conclusion, inductive logic, duckbilled platypi

Click to edit the document details