fallacies of induction these arguments are supposed to raise the probability of their conclusion but...

Fallacies of Induction

These arguments are supposed to raise the probability of their conclusion but fail almost entirely.

Chapter 7

© 2015 McGraw-Hill Higher Education. All rights reserved.

Fallacies of Generalization Hasty Generalization (Generalizing from Too Few

Cases Argument by Anecdote Fallacy of Small SampleBut good reasoning can come from small samples

Generalizing from Exceptional Cases Biased Sample Self-Selection

Accident

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Generalizing from Too Few Cases

Often called Hasty Generalization Example:

“The food in this town is lousy, judging from this meal.”

© 2015 McGraw-Hill Higher Education. All rights reserved. 3


Which is Hasty Generalizing?

1. “Like, EVERYONE in New York is friendly! Everybody I met was as nice as can be!”

2. “This beer is terrible. I can tell that from one sip.”

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2 is okay because the “population” (this beer) is homogeneous.


Is this hasty generalizing?

• “Man! Old folks around here are terrible drivers! Did you see that old coot passing me on the right??!!”

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Yes.

Hasty Generalization (continued)

Two common versions of hasty generalization:

Argument by AnecdoteFallacy of Small Sample



Sometimes Hasty Generalizing takes the form known as:

“ARGUING FROM ANECDOTE.”

What is an anecdote?

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An anecdote is a story…

“One time I saw this airplane parked right in the middle of the tarmac in everyone’s way. I asked who it belonged to. Someone told me it was John Travolta’s. Folks, that’s the trouble with these Hollywood liberals. They only care about themselves.”

—Rush Limbaugh, paraphrased

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ARGUING FROM ANECDOTE is just a form of Hasty Generalizing.

Now you know why statisticians say “Anecdotes don’t prove anything.”

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Argument by Anecdote

“The IRS isn’t interested in the big corporations, just middle-class taxpayers like you and me. I was audited last year—you ever hear of Exxon-Mobile getting nailed?”



“This global warming stuff…what a crock. We had the coldest January on record right here in Columbus last year.”

Anecdotes are often used to “refute” a body of contrary evidence.

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It won’t work!!!

“ARGUING FROM ANECDOTE” is nothing more than generalizing from a single case or two.

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Watch out for a small sample!

Small samples provide very weak support!

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Fallacy of Small Sample “This Chihuahua bites. Therefore, all

Chihuahuas bite.”


Good reasoning from small samples

When a sample is taken from a homogeneous population, a generalization can be made from the sample.

“This sip of coffee is delicious, therefore all sips of coffee from this cup will be delicious.”


Generalizing from Exceptional Cases

A fallacy occurs when a speaker or writer attempts to support a general statement by citing an atypical supporting case.


Generalizing from Exceptional Cases

Two varieties of generalizing from exceptional cases:

Biased Sample Self-Selection Fallacy


Fallacy of Biased Sample Basing a generalization about a large

heterogeneous population on an atypical or skewed sample.

Example:

“Judging from what car dealers say, most business people now think the economy is improving.”


Fallacy of Biased Sample Example:

“George W. Bush was really popular in South America. Look at how many people came out to cheer when he went down there.”

The sample (South Americans outside cheering) UNDER-REPRESENTS South Americans who don’t feel like going out. It is atypical.


Self-Selection Fallacy Example:

“Most Americans have a favorable view of the president as a person, judging from an online survey conducted by CNN.”

A Self-Selection Fallacy occurs when generalizing from a sample whose members are in the sample by their own choice.


Accident The Fallacy of Accident occurs when it is

assumed that a general statement applies to a specific case even though that case may be an exception.

Example:

“Everyone should have access to a college education. Therefore, anyone who applies should be admitted to Cal Poly.”


Accident Another example:

“This city has a very high crime rate; therefore it will be dangerous to shop in this neighborhood.”

Fallacy of Accident! To infer from the city’s overall high crime rate,

considered in and of itself without regard to anything else, that a particular location in the city has a high crime rate is to commit this fallacy.



Weak Analogy Arguments based on debatable or

unimportant similarities between things. Example:

“My mom is just like Adolf Hitler. I doubt she will let me go out with you guys.”

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Weak analogy?

“Hey, the ice caps on Mars are melting, and it isn’t due to fossil fuel emissions!! So melting ice caps here aren’t due to fossil fuel emissions either!”

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Weak Analogy! Earth and Mars are physically similar– a

good reason to think ice caps COULD melt on Earth in the absence of fuel emissions.

Not a good argument for thinking fuel emissions DIDN’T cause melting here.

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More Fallacies Fallacies based on authority. Fallacies based on popularity or common

practice.

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Fallacious Appeal to Authority Trying to support a contention by offering

as evidence the opinion of a nonauthoritative source.

Example:

“My father thinks the president lied. Therefore the president lied.”

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THREE LOOK-ALIKE MISTAKES

Fallacious Appeal to PopularityFallacious Appeal to Common PracticeFallacious Appeal to Tradition

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Fallacious Appeal to Popularity Treating an issue that cannot be settled

by public opinion as if it can. Example:

“Hondas get great gas mileage. Everyone knows that.”



Fallacious Appeal to Common Practice Trying to justify a practice on the grounds

that it is traditional or is commonly practiced.

Example:

“This is the right way. This is the way it has always been done.”

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Fallacious Appeal to Tradition

Doing X is a tradition. Therefore it should continue being a tradition.

Example:

“Traditionally marriage has been restricted to heterosexual couples. Therefore it’s right to restrict marriage to heterosexual couples.”

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BANDWAGON FALLACY

Similar to the fallacious appeal to popularity.

“You shouldn’t eat at Applebee’s. No right thinking person does.”

The Bandwagon Fallacy plays on our desire to be in step with popular opinion.

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Cause and Effect Fallacies

“After I took Zicam my cold went away fast. Therefore taking Zicam caused my cold to go away fast.”

How do you know that?

This is a cause/effect fallacy.

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Post Hoc, Ergo Propter Hoc

Assuming that the fact that one event came after another establishes that it was caused by it.

Example:

“After I played poker my cold went away fast. Therefore playing poker caused my cold to go away fast.”

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Cum Hoc, Ergo Propter Hoc

Assuming that the fact that one event happened around the same time as another establishes that it was caused by it.

Example:

“John had a heart attack while he was saying a prayer. Therefore the prayer caused the heart attack.”

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“Every day the sun comes up right after the rooster crows; therefore the rooster causes the sun to come up.”

Correlation does not prove causation! This is post hoc, ergo propter hoc. Even the fact that one event invariably

follows another still does not prove that the first caused the second.

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Varieties of post hoc fallacies—Overlooking regression

Example: After a terrible evening with mosquitoes, you wear

a copper ‘Mosquito Be-gone’ bracelet. The mosquitoes didn’t seem so bad after that. You conclude that the bracelet works.

[If the average value of a variable is atypical on one measurement, it is likely to be less atypical on a subsequent measurement.]

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Varieties of post hoc fallacies—Overlooking random variation

Example: “In our tests, we asked randomly selected men to drive a golf ball

as far as they could. We then had them wear our magnetic bracelet and try again. On the second occasion the men hit the ball an average of ten feet further. Our bracelet can lengthen your drive as well.”

[The average length of the drive will vary randomly from test to test.]

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Varieties of cum hoc fallacies—overlooking reversed causation.

Example: “People who walk long distances enjoy good health. Therefore walking long distances will make you healthy.”

[Perhaps being healthy makes people more inclined to take long walks]

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Varieties of post and cum hoc fallacies—Overlooking coincidence

Example:

“After Susan threw out the chain letter, she was in an automobile accident. Therefore throwing out the chain letter caused her to get in an automobile accident.”

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Another example:

“I got cancer when I lived under a high-voltage power line. Therefore, the high voltage power line caused my cancer.”

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Another Example:

“Chimney fires and long underwear purchases increase in frequency at the very same time. Therefore chimney fires cause people to buy long underwear.”

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Varieties of post and cum hoc fallacies—Overlooking a common cause

Example:

“I left the lights on when I went to bed. Next morning I woke up with a headache. Therefore sleeping with the lights on causes headaches.”

[Perhaps being tired caused both headache and leaving the lights on]

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Argument by Anecdote (Causal Variety)

It is a fallacy to try to support (or disprove) a cause-and-effect claim by telling a story.

Example:I’ve heard doctors say eating red meat increases your risk of heart disease, but don’t believe it. My uncle lived to be a 100 and ate red meat 3 times a day. He didn’t die of a heart attack.

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Slippery Slope A fallacy that occurs when a speaker or writer

rests a conclusion on an unsupported warning that is controversial and tendentious, to the effect that something will progress by degrees to an undesirable outcome.

Example:

“If we legalize pot, the next thing ya know we will be making meth legal, then heroin—it will never end.”


Slippery Slope Another example:

“Twenty percent? You want to tip her 20%? Hey, next thing you’ll want to tip 25%! And then 30%! It will never end.”



Untestable Explanation Some explanations are vague or circular

or not testable even in principle. Such explanations are said to commit the

fallacy of Untestable Explanation.

Example:“He has heart issues because of sins done in a previous life.”

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What do these two explanations have in common?

“The attack on the Twin Towers was God’s way of telling us we are sinners.”

“What brought Todd and Brenda together? Fate.”

One thing they have in common:

Neither one can be TESTED.

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Untestable ExplanationA different example:

“Men are biologically weaker than women and that’s why they don’t live as long.”—An “expert” quoted in Weekly World News

Some assertions can’t be tested due to their vagueness.

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fallacies of induction these arguments are supposed to raise the probability of their conclusion but...

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