LIS 504 - Fallacies
A fallacy is an error in logic.
There are many different kinds of fallacy
for which researchers must watch out,
some of which are outlined below.
The ecological and exception fallacies
The ecological fallacy is an error in deduction;
it involves making conclusions about individuals
based only on analyses of group data.
Noting that the average library user takes a week to read a book
and concluding that books should be due
a week after they are taken out
would be an example of this fallacy.
The exception fallacy is an error in induction;
it involves making a conclusion about a group
on the basis of exceptional cases.
Concluding that a book is not acceptable to your community
because you have received complaints about it
could be an example of this fallacy.
Ex post facto hypothesizing
In ex post facto hypothesizing,
the same data that were used to develop a hypothesis
are used to test it.
The trouble is that,
while a hypothesis can be tailor made to fit almost any data,
it may not hold up for other, similar data
and, as a result, it may be fairly useless.
in looking at data on online searches
that failed on account of technical reasons,
we might note a week-by-week sequence such as:
It might strike us that the rate is declining somewhat,
and, if we tested this hypothesis on the data that produced it,
we would indeed find
a marginally statistically significant correlation
But it might be that the correlation
is just the result of random or atypical events.
After another two weeks,
we might see the following pattern emerge:
Now the correlation is weaker
and no longer statistically significant
Our hypothesis that failures are generally declining
is no longer supported by the data.
The hidden factor fallacy
Paying insufficient attention to the third criterion of
- the absence of other plausible causal agents -
can lead to the hidden factor fallacy.
research might show that children who were exposed to computers
subsequently engaged in more Web searching as teenagers
than those who were not;
but a conclusion that early computer exposure
causes increased later use of computer search engines
might be wrong,
because both might be due to a third factor,
such as the socioeconomic status of parents.
The regression fallacy
If a researcher selects items with extreme scores at one point
and then looks at the scores of the same, or related, items
at another point in time,
we can expect that the second set of scores will average
closer to the mean value for the population as a whole.
Failing to recognize this natural regression toward the
constitutes the regression fallacy.
To take a silly example,
suppose we have a group of people throw a die
and then select the group who got low scores
(1 or 2).
We then apply a "treatment",
such as giving them all "magic feathers".
They then each roll the die again.
We might then end up with a result like the following:
||Without magic feather
||With magic feather
Substitute some real-life variables,
such as "visits to the library"
for "value of throw"
and "library promotion campaign"
for "magic feather"
and you can see the potential dangers of this fallacy.
Last updated November 2, 2000.
This page maintained by
Prof. Tim Craven
E-mail (text/plain only): firstname.lastname@example.org
Faculty of Information and
University of Western
Canada, N6A 5B7