Select a logical fallacy from this week’s reading assignment in the textbook or from one of the other internet links posted under Readings and Resources; there are many for you to choose from. Explain the fallacy and how it defies logic, and provide an example of its use that you have identified from current events, film or literature, or from your personal experience. Let’s see how many different logical fallacies we can identify and discuss; this will raise awareness of informal fallacies for all of us.

readings

In this and the remaining sections of this chapter, we will consider some formal

fallacies of probability. These fallacies are easy to spot once you see them, but

they can be difficult to detect because of the way our minds mislead us—

analogous to the way our minds can be misled when watching a magic trick. In

addition to introducing the fallacies, I will suggest some psychological

explanations for why these fallacies are so common, despite how easy they are

to see once we’ve spotted them.

The conjunction fallacy is best introduced with an example.6

Linda is 31 years old, single, outspoken, and very bright. She majored in

philosophy. As a student, she was deeply concerned with issues of

6 The following famous example comes from Tversky, A. and Kahneman, D. (1983). Extension

versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review,

90(4), 293–315.

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discrimination and social justice, and also participated in anti-nuclear

demonstrations.

Given this information about Linda, which of the following is more

probable?

a. Linda is a bank teller.

b. Linda is a bank teller and is active in the feminist movement.

If you are like most people who answer this question, you will answer “b.” But

that cannot be correct because it violates the basic rules of probability. In

particular, notice that option b contains option a (i.e., Linda is a bank teller). But

option b also contains more information—that Linda is also active in the feminist

movement. The problem is that a conjunction can never be more probable than

either one of its conjuncts. Suppose we say it is very probable that Linda a bank

teller (how boring, given the description of Linda which makes her sound

interesting!). Let’s set the probability low, say .4. Then what is the probability of

her being active in the feminist movement? Let’s set that high, say .9. However,

the probability that she is both a bank teller and active in the feminist movement

must be computed as the probability of a conjunction, like this:

.4 × .9 = .36

So given these probability assignments (which I’ve just made up but seem fairly

plausible), the probability of Linda being both a bank teller and active in the

feminist movement is .36. But .36 is a lower probability than .4, which was the

probability that she is bank teller. So option b cannot be more probable than

option a. Notice that even if we say it is absolutely certain that Linda is active in

the feminist movement (i.e., we set the probability of her being active in the

feminist movement at 1), option b is still only equal to the probability of option

a, since (.4)(1) = .4.

Sometimes it is easy to spot conjunction fallacies. Here is an example that

illustrates that we can in fact easily see that a conjunction is not more probable

than either of its conjuncts.

Mark is drawing cards from a shuffled deck of cards. Which is more

probable?

a. Mark draws a spade

b. Mark draws a spade that is a 7

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In this case, it is clear which of the options is more probable. Clearly a is more

probable since it requires less to be true. Option a would be true even if option

b is true. But option a could also be true even if option b were false (i.e., Mark

could have drawn any other card from the spades suit). The chances of drawing

a spade of any suit is ¼ (or .25) whereas the chances of drawing a 7 of spades is

computed using the probability of the conjunction:

P(drawing a spade) = .25

P(drawing a 7) = 4/52 (since there are four 7s in the deck of 52) = .077

Thus, the probability of being both a spade and a 7 = (.25)(.077) = .019

Since .25 > .019, option a is more probable (not that you had to do all the

calculations to see this).

Thus there are cases where we can easily avoid committing the conjunction

fallacy. So what is the difference between this case and the Linda case? The

Nobel Prize-winning psychologist, Daniel Kahneman (and his long-time

collaborator, Amos Tversky), has for many years suggested a psychological

explanation for this difference. The explanation is complex, but I can give you

the gist of it quite simply. Kahneman suggests that our minds are wired to find

patterns and many of these patterns we find are based on what he calls

“representativeness.” In the Linda case, the idea of Linda being active in the

feminist movement fits better with the description of Linda as a philosophy

major, as being active in social justice movements, and, perhaps, as being

single. We build up a picture of Linda and then we try to match the descriptions

to her. “Bank teller” doesn’t really match anything in the description of Linda.

That is, the description of Linda is not representative of a bank teller. However,

for many people, it is representative of a feminist. Thus, our minds more or less

automatically see the match between representativeness of the description of

Linda and option b, which mentions she is a feminist. Kahneman thinks that in

cases like these, our minds substitute a question of representativeness for the

question of probability, thus answering the probability question incorrectly.7

We

are distracted from the probability question by seeking representativeness,

which our minds more automatically look for and think about than probability.

For Kahneman, the psychological explanation is needed to explain why even

7 Kahneman gives this explanation numerous places, including, most exhaustively (and for a

general audience) in his 2011 book, Thinking Fast and Slow. New York, NY: Farrar, Straus and

Giroux.

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trained mathematicians and those who deal regularly with probability still

commit the conjunction fallacy. The psychological explanation that our brains

are wired to look for representativeness, and that we unwittingly substitute the

question of representativeness for the question of probability, explains why even

experts make these kinds of mistakes.

3.7 The base rate fallacy

Consider the following scenario. You go in for some testing for some health

problems you’ve been having and after a number of tests, you test positive for

colon cancer. What are the chances that you really do have colon cancer? Let’s

suppose that the test is not perfect, but it is 95% accurate. That is, in the case of

those who really do have colon cancer, the test will detect the cancer 95% of the

time (and thus miss it 5% of the time). (The test will also misdiagnose those who

don’t actually have colon cancer 5% of the time.) Many people would be

inclined to say that, given the test and its accuracy, there is a 95% chance that

you have colon cancer. However, if you are like most people and are inclined to

answer this way, you are wrong. In fact, you have committed the fallacy of

ignoring the base rate (i.e., the base rate fallacy).

The base rate in this example is the rate of those who have colon cancer in a

population. There is very small percentage of the population that actually has

colon cancer (let’s suppose it is .005 or .5%), so the probability that you have it

must take into account the very low probability that you are one of the few that

have it. That is, prior to the test (and not taking into account any other details

about you), there was a very low probability that you have it—that is, a half of

one percent chance (.5%). The test is 95% accurate, but given the very low prior

probability that you have colon cancer, we cannot simply now say that there is a

95% chance that you have it. Rather, we must temper that figure with the very

low base rate. Here is how we do it. Let’s suppose that our population is

100,000 people. If we were to apply the test to that whole population, it would

deliver 5000 false positives. A false positive occurs when a test registers that

some feature is present, when the feature isn’t really present. In this case, the

false positive is when the test for colon cancer (which will give false positives in

5% of the cases) says that someone has it when they really don’t. The number of

people who actually have colon cancer (based on the stated base rate) is 500,

and the test will accurately identify 95 percent of those (or 475 people). So what

you need to know is the probability that you are one who tested positive and Chapter 3: Evaluating inductive arguments and probabilistic and statistical fallacies

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actually has colon cancer rather than one of the false positives. And what is the

probability of that? It is simply the number of people who actually have colon

cancer (500) divided by the number that the test would identify as having colon

cancer. This latter number includes those the test would misidentify (5000) as

well as the number it would accurately identify (475)—thus the total number the

test would identify as having colon cancer would be 5475. So the probability

that you have it, given the positive test = 500/5475 = .091 or 9.1%. So the

probability that you have cancer, given the evidence of the positive test is 9.1%.

Thus, contrary to our initial reasoning that there was a 95% chance that you have

colon cancer, the chance is only a tenth of that—it is less than 10%! In thinking

that the probability that you have cancer is closer to 95% you would be ignoring

the base rate of the probability of having the disease in the first place (which, as

we’ve seen, is quite low). This is the signature of any base rate fallacy. Before

closing this section, let’s look at one more example of a base rate fallacy.

Suppose that the government has developed a machine that is able to detect

terrorist intent with an accuracy of 90%. During a joint meeting of congress, a

highly trustworthy source says that there is a terrorist in the building. (Let’s

suppose, for the sake of simplifying this example, that there is in fact a terrorist

in the building.) In order to determine who the terrorist is, the building security

seals all the exits, rounds up all 3000 people in the building and uses the

machine to test each person. The first 30 people pass without triggering a

positive identification from the machine, but on the very next person, the

machine triggers a positive identification of terrorist intent. The question is:

what are the chances that the person who set off the machine really is a

terrorist?8

Consider the following three possibilities: a) 90%, b) 10%, or c) .3%.

If you answered 90%, then you committed the base rate fallacy again. The

actually answer is “c”—less than 1%! Here is the relevant reasoning. The base

rate here is that it is exceedingly unlikely that any individual is a terrorist, given

that there is only one terrorist in the building and there are 3000 people in the

building. That means the probability of any one person being a terrorist, before

any results of the test, is exceedingly low: 1/3000. Since the test is 90%

accurate, that means that out of the 3000 people, it will misidentify 10% of them

as terrorists = 300 false positives. Assuming the machine doesn’t misidentify the

one actual terrorist, the machine will identify a total of 301 individuals as those

“possessing terrorist intent.” The probability that any one of them actually

8 This example is taken (with certain alterations) from:

http://news.bbc.co.uk/2/hi/uk_news/magazine/8153539.stm

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possesses terrorist intent is 1/301 = .3%. So the probability is drastically lower

than 90%. It’s not even close. This is another good illustration of how far off

probabilities can be when the base rate is ignored.

3.8 The small numbers fallacy

Suppose a study showed that of the 3,141 counties of the United States, the

incidence of kidney cancer was lowest in those counties which are mostly rural,

sparsely populated, and located in traditionally Republican states. In fact, this is

true.9

What accounts for this interesting finding? Most people would be

tempted to look for a causal explanation—to look for features of the rural

environment that account for the lower incidence of cancer. However, they

would be wrong (in this case) to do so. It is easy to see why once we consider

the counties that have the highest incidence of kidney cancer: they are counties

that are mostly rural, sparsely populated, and located in traditionally Republican

states! So whatever it was you thought might account for the lower cancer rates

in rural counties can’t be the right explanation, since these counties also have

the highest rates of cancer. It is important to understand that it isn’t the same

counties that have the highest and lowest rates—for example, county X doesn’t

have both a high and a low cancer rate (relative to other U.S. counties). That

would be a contradiction (and so can’t possibly be true). Rather, what is the

case is that counties that have the highest kidney cancer rates are “mostly rural,

sparsely populated, and located in traditionally Republican states” but also

counties that have the lowest kidney cancer rates are “mostly rural, sparsely

populated, and located in traditionally Republican states.” How could this be?

Before giving you the explanation, I’ll give you a simpler example and see if you

can figure it out from that example.

Suppose that a jar contains equal amounts of red and white marbles.

Jack and Jill are taking turns drawing marbles from the jar. However, they

draw marbles at different rates. Jill draws 5 marbles at a time while Jack

draws 2 marbles at a time. Who is more likely to draw either all red or all

white marbles more often: Jack or Jill?10

The answer here should be obvious: Jack is more likely to draw marbles of all

the same color more often, since Jack is only drawing 2 marbles at a time. Since

9 This example taken from Kahneman (2011), op. cit., p. 109.

10 This example is also taken (with minor modifications) from Kahneman (2011), p. 110.

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Jill is drawing 5 marbles at a time, it will be less likely that her draws will yield

marbles of all the same color. This is simply a fact of sampling and is related to

the sampling errors discussed in section 3.1. A sample that is too small will tend

not to be representative of the population. In the marbles case, if we view

Jack’s draws as samples, then his samples, when they yield marbles of all the

same color, will be far from representative of the ratio of marbles in the jar, since

the ratio is 50/50 white to red and his draws sometimes yield 100% red or 100%

white. Jill, on the other hand, will tend not to get as unrepresentative a sample.

Since Jill is drawing a larger number of marbles, it is less likely that her samples

would be drastically off in the way Jack’s could be. The general point to be

taken from this example is that smaller samples tend to the extremes—both in

terms of overrepresenting some feature and in underrepresenting that same

feature.

Can you see how this might apply to the case of kidney cancer rates in rural,

sparsely populated counties? There is a national kidney cancer rate which is an

average of all the kidney cancer rates of the 3,141 counties in the U.S. Imagine

ranking each county in terms of the cancer rates from highest to lowest. The

finding is that there is a relatively larger proportion of the sparsely populated

counties at the top of this list, but also a relatively larger proportion of the

sparsely populated counties at the bottom of the list. But why would it be that

the more sparsely populated counties would be overrepresented at both ends

of the list? The reason is that these counties have smaller populations, so they

will tend to have more extreme results (of either the higher or lower rates). Just

as Jack is more likely to get either all white marbles or all red marbles (an

extreme result), the less populated counties will tend to have cancer rates that

are at the extreme, relative to the national average. And this is a purely

statistical fact; it has nothing to do with features of those environments causing

the cancer rate to be higher or lower. Just as Jack’s extreme draws have

nothing to do with the way he is drawing (but are simply the result of statistical,

mathematical facts), the extremes of the smaller counties have nothing to do

with features of those counties, but only with the fact that they are smaller and

so will tend to have more extreme results (i.e., cancer rates that are either higher

or lower than the national average).

The first take home lesson here is that smaller groups will tend towards the

extremes in terms of their possession of some feature, relative to larger groups.

We can call this the law of small numbers. The second take home message is

that our brains are wired to look for causal explanations rather than Chapter 3: Evaluating inductive arguments and probabilistic and statistical fallacies

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mathematical explanations, and because of this we are prone to ignore the law

of small numbers and look for a causal explanation of phenomena instead. The

small numbers fallacy is our tendency to seek a causal explanation for some

phenomenon when only the law of small numbers is needed to explain that

phenomenon.

We will end this section with a somewhat humorous and incredible example of a

small numbers bias that, presumably, wasted billions of dollars. This example,

too, comes from Kahneman, who in turn heard the anecdote from some of his

colleagues who are statisticians.11 Some time ago, the Gates foundation (which

is the charitable foundation of Microsoft founder, Bill Gates) donated 1.7 billion

to research a curious finding: smaller schools tend to be more successful than

larger schools. That is, if you consider a rank ordering of the most successful

schools, the smaller schools will tend to be overrepresented near the top (i.e.,

there is a higher proportion of them near the top of the list compared to the

proportion of larger schools at the top of the list). This is the finding that the

Gates Foundation invested 1.7 billion dollars to help understand. In order to do

so, they created smaller schools, sometimes splitting larger schools in half.

However, none of this was necessary. Had the Gates Foundation (or those

advising them) looked that the characteristics of the worst schools, they would

have found that those schools also tended to be smaller! The “finding” is

merely a result of the law of small numbers: smaller groups tend towards the

extremes (on both ends of a spectrum) more so than larger groups. In this case,

the fact that smaller schools tend to be both more successful and less successful

is explained in the same way as we explain why Jack tends to get either all red

or all white marbles more often than Jill.

3.9 Regression to the mean fallacy

Humans are prone to see causes even when no such cause is present. For

example, if I have just committed some wrong and then immediately after the

thunder cracks, I may think that my wrong action caused the lightning (e.g.,

because the gods were angry with me). The term “snake oil” refers to a product

that promises certain (e.g., health) benefits but is actually fraudulent and has no

benefits whatsoever. For example, consider a product that is supposed to help

you recover from a common cold. You take the medicine and then within a few

11 Kahneman (2011), pp. 117-118.

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days, you are all better! No cold! It must have been the medicine. Or maybe

you just regressed to the mean. Regression to the mean describes the

tendency of things to go back to normal or to return to something close to the

relevant statistical average. In the case of a cold, when you have a cold, you are

outside of the average in terms of health. But you will naturally return to the

state of health, with or without the “medicine.” If anyone were to try to

convince you to buy such a medicine, you shouldn’t. Because the fact that you

got better from your cold more likely has to do with the fact that you will

naturally regress to the mean (return to normal) than it has to do with the special

medicine.

Another example. Suppose you live in Lansing and it has been over 100

degrees for two weeks straight. Someone says that if you pay tribute and do a

special dance to Baal, the temperature will drop. Suppose you do this and the

temperature does drop. Was it Baal or just regression to the mean? Probably

regression to the mean, unless we have some special reason for thinking it is

Baal. The point is, extreme situations tend to regress towards less extreme,

more average situations. Since it is very rare for it to ever be over 100 degrees

in Lansing, the fact that the temperature drops is to be expected, regardless of

one’s prayers to Baal.

Suppose that a professional golfer has been on a hot streak. She has been

winning every tournament she enters by ten strokes—she’s beating the

competition like they were middle school golfers. She is just playing so much

better than them. Then something happens. The golfer all of a sudden starts

playing like average. What explains her fall from greatness? The sports

commentators speculate: could it be that she switched her caddy, or that it is

warmer now than is was when she was on her streak, or perhaps it was fame that

went to her head once she had started winning all those tournaments? Chances

are, none of these are the right explanation because no such explanation is

needed. Most likely she just regressed to the mean and is now playing like

everyone else—still like a pro, just not like a golfer who is out of this world

good. Even those who are skilled can get lucky (or unlucky) and when they do,

we should expect that eventually that luck will end and they will regress to the

mean.

As these examples illustrate, one commits the regression to the mean fallacy

when one tries to give a causal explanation of a phenomenon that is merely

statistical or probabilistic in nature. The best way to rule out that something is Chapter 3: Evaluating inductive arguments and probabilistic and statistical fallacies

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not to be explained as regression to the mean is by doing a study where one

compares two groups. For example, suppose we could get our snake oil

salesman to agree to a study in which a group of people who had colds took the

medicine (experimental group) and another group of people didn’t take the

medicine or took a placebo (control group). In this situation, if we found that

the experimental group got better and the control group didn’t, or if the

experimental group got better more quickly than the control group, then

perhaps we’d have to say that maybe there is something to this snake oil

medicine. But without the evidence of a control for comparison, even if lots of

people took the snake oil medicine and got better from their colds, it wouldn’t

prove anything about the efficacy of the medicine.

3.10 Gambler’s fallacy

The gambler’s fallacy occurs when one thinks that independent, random events

can be influenced by each other. For example, suppose I have a fair coin and I

have just flipped 4 heads in a row. Erik, on the other hand, has a fair coin that he

has flipped 4 times and gotten tails. We are each taking bets that the next coin

flipped is heads. Who should you bet flips the head? If you are inclined to say

that you should place the bet with Erik since he has been flipping all tails and

since the coin is fair, the flips must even out soon, then you have committed the

gambler’s fallacy. The fact is, each flip is independent of the next, so the fact

that I have just flipped 4 heads in a row does not increase or decrease my

chances of flipping a head. Likewise for Erik. It is true that as long as the coin is

fair, then over a large number of flips we should expect that the proportion of

heads to tails will be about 50/50. But there is no reason to expect that a

particular flip will be more likely to be one or the other. Since the coin is fair,

each flip has the same probability of being heads and the same probability of

being tails—50%.

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