The purpose of inductive argument is to take evidence from observed
cases and draw probable conclusions about unobserved cases. Inductive
arguments move beyond what the premises contain. This means there
is always an element of uncertainty with induction. It is possible
for the premises of an inductive argument to be true and still
have a conclusion that is false.
Inductive arguments offered in support of a general claim. This
is a broad category which includes the more specific category of
statistical induction (discussed below). It is an argument where
some observations are made (sample) and then conclusions are made
concerning the larger category (population).
All the ducks we've seen have feathers. Therefore, it
is likely that all ducks have feathers.
A comparison in which something which is easily understood is
used to explain something more difficult to understand by comparing
"The president is the captain of the ship of state." This
implies that there are certain similarities between overseeing
the operation of a ship and being the chief executive of a nation.
Some of these similarities are metaphorical, for instance a ship
heads in a literal direction, while a nation doesn't actually move,
but figuratively "changes direction" according to changes
What do frog legs taste like? You might say they taste
like a combination of chicken and shrimp. Though not perfect in
describing the flavor, it at least helps the person to understand
by referring to something that they know about.
Even the strongest analogies have dissimilarities as well as similarities.
One must be careful about analogies in several ways:
- Are the similarities really similar?
- Are the similarities relevant?
- What are the relevant dissimilarities?
A bad analogy is considered a fallacy because it leads to a false
conclusion. For instance, Vice-president Bush was once asked why
he did not argue with President Reagan about the Iran-Contra policy.
He responded, "You don't tackle your own quarterback." He
suggested that being the president is similar to being the quarterback
of a football team and the vice-president is similar to another
player on the same team. Now it is true that one doesn't normally
tackle their own quarterback. However, it might be appropriate
if the quarterback was running for the wrong end zone.
Statistical induction is a particular type of generalization where
statistical evidence from a sample is said to give meaningful information
about the target population. Statistics are often very useful,
but there can be pitfalls. The subject of statistics is quite complex,
requiring entire courses to fully understand.
For this course is
will be useful to have several simple questions which one can ask
regarding any statistic. (From How To Lie With Statistics, by Darryl
- 1) Who says so?
Is this a disinterested source? or do they have something to
- 2) How do they know?
How was the data gathered? Is it possible to know? Is the sample
large enough? Were all the relevant considerations taken into
- 3) Did somebody change the subject?
This is especially important there are statistics being compared.
For instance, more people died in traffic accidents in the U.S.
than in the Vietnam war during the same period. These things
aren't comparable for several reasons. First, accidents are just
that - accidents, while war is intentional killing. Second, there
were more people driving than engaged in the war. Third, it is
unclear whether the statistic refers only to American soldiers
or if it includes all the soldiers on both sides and civilian
- 4) What's missing?
What isn't told is often more important that what is. For instance,
if the base figure is very low, any change will be a large percentage.
Example: Several years ago, a town in Nevada became "the muder
capital of America." This was based on the fact that residents
had a one in fifteen chance of being murdered. What was missing
were these more important facts: Only fifteen people lived
in the town. One was recently murdered. There had not been any
other murders there for over forty years.
- 5) Does it make sense?
Example: A statistic that continues to crop up says that 150,000
girls die each year from anorexia. It is clear that this could
not be true when one compares that to the number of highway fatalities
in the U.S., which happens to be around 40,000 each year. Common
sense tells us that the 150,000 figure cannot be correct. It
doesn't make sense.
Higher induction combines evidence of various kinds with reasoning.
For instance, let's suppose you just bought a new car. It's so
new that nothing has ever gone wrong with it. That's the data.
Generalizing from our current data to the future, we would conclude
that because the car has never broken down, that it will probably
never break down. Well, we know that isn't the way things work.
Higher induction combines the information about this car with what
we know about mechanical things in general. We know from higher
induction that mechanical things wear out and tend to eventually
break. So, we infer that this car will eventually require repair.