There are lies, damn lies – and statistics.
-Mark Twain

Strengthen and weaken questions usually revolve around causal issues (what causes what). Before moving on to Strengthen and Weaken, we’re going to discuss causal errors in depth and their typical origin: faulty statistical reasoning.

## 1. Biased Sample Fallacy

Statistical fallacies are common on the LSAT as a form of causal fallacies. The biased sample fallacy is committed whenever the data for a statistical inference is drawn from a sample that is not representative of the population under consideration.

A recent study showed that over 60% of Oregon residents watched cartoons. Based on this study, executives at Cartoon Channel spent \$10 million to expand their access to Oregonians, who appear to be avid fans of cartoons.

Note that this survey doesn’t say anything about the specific Oregon residents polled. Are they school children? The results seem to indicate so. A sample must be representative of the overall population that we want to study in order to make a general conclusion.

Here is another example:

In a recent survey conducted by Wall Street Weekly of its readers, 80% of the respondents indicated their strong disapproval of increased capital gains taxes. This survey clearly shows that increased capital gains taxes will be met with strong opposition from the electorate.

The data for the conclusion of this argument is drawn from a sample that is not representative of the entire electorate. The survey was conducted by people who invest and not random members of the electorate. People who read about investing are more likely to have an opinion on the topic of taxes on investments than the population at large.

John: I don’t want to die in an accident. Every few days on the TV news, I hear of a major plane crash somewhere in the world. I would never fly in planes; they are too dangerous.

Ted: Nonsense, statistics show that airplanes are the safest mode of transportation on a per-mile basis.

John: The answer then is not to travel such long distances.

### Analysis

Analysis: John is pointing out that plane crashes are always in the news, therefore they must be very dangerous. The TV news, however, is a biased sample of all accidents. Minor traffic fatalities around the world rarely make the news, but plane crashes do.

Ted points out the obvious fact that on a per-mile basis, planes are safer, yet planes can travel ten thousand miles, so a long trip does entail risk. John’s final analysis is to play it safe and not to travel long distances at all.

Next LSAT: Jun 10/Jun 11

## 3. Insufficient Sample Fallacy

The Fallacy of the Insufficient Sample (also called the “hasty generalization”) is committed whenever an inadequate sample is used to justify the conclusion drawn. In a Biased Sample, people are pulled from a non-representative group; in an Insufficient Sample, not enough people are polled to yield a statistically significant result.

I have worked with three people from New York City and found them to be obnoxious, pushy, and rude. It is obvious that people from New York City have a bad attitude.

Observations of three people are not sufficient to support a conclusion about 10 million. Bad luck could account for meeting three bad people. Try this one:

After living and working in New York City for 12 years, I have met thousands of people and, with very rare exceptions, I have found them to be obnoxious, pushy, and rude. It is obvious that people from New York City have a bad attitude

Anecdotal
Using personal experience or an isolated example instead of a sound argument or compelling evidence.

The Gambler’s Fallacy

Thinking that there are streaks on independent items, such as dice rolling.

## 4. Correlation vs. Causation

A correlation is a statistical linking between two items that seem to be parallel. One of the LSAT’s “Greatest Hits” you see time and time again is the attempt to link up two separate items that seem to statistically correlate and then establish one of the two as the “cause.”

The relation between a cause and an association is difficult. Here’s a simple example:

1. Heavier people tend to be taller.
2. Weight is correlated with height.
3. Gaining weight will make you taller.

This argument assumes a relationship between correlated data and thus concludes that by changing one element, you can change the other.

Another obvious one:

1. More fire trucks tend to be at more serious fires.
2. We can reduce the severity of fires by reducing the number of fire trucks.

Here is a more challenging example:

1. Young people who watch more TV violence are more likely to engage in violence.
2. The recent increase in TV violence is associated with an increase in violence society-wide.
3. If children would watch less TV violence, they would be less violent.

### Explanation

This one seems intuitive enough and it’s the “sentimental favorite,” but the reality is that (3) can’t be proven from (1) and/or (2). You can’t assume that just because things correlate you can change one factor and it will automatically change the other. Children who watch large amounts of TV may have inattentive parents, and this may be the underlying hidden causal factor — not watching too much TV violence in itself. This argument could use more evidence, like a study showing that violent children are more successfully rehabilitated by cutting off violent shows.

Example

Studies have shown that men aged 18-27 who have owned a pet for at least 2 years before marrying are 35% less likely to divorce. Researchers conclude that caring for a pet prepares men for long-term, healthy relationships in marriage.

Which of the following, if true, most strengthens the conclusion that men who have owned pets are prepared for healthy marriages?

1. Studies have shown that pet ownership drastically reduces daily stress levels.
2. Many successful marriages are based on emotional investment in a common interest, such as a pet.
3. Many men who have been married for 25 years or more continue to own pets.
4. Men who have not owned dogs for at least two years before marrying are more likely to divorce.
5. Men whose wives owned a pet for at least two years are equally as unlikely to divorce.

### Explanation

Situation: Researchers have concluded that men who have owned a pet for at least 2 years are prepared for healthy marriages.

Reasoning: Which option most strengthens the conclusion? Researchers base their conclusion on an assumed connection between sustained care for a pet and care for a spouse. Men who care for pets before marriage, the argument runs, are also statistically more likely to sustain marriage relationships. The problem is that correlation doesn’t prove causality so that link alone is not enough.

1. While this may be true, it does not introduce additional evidence to support the conclusion.
2. This option does not address the question of why men who own pets are less likely to divorce.
3. The question concerns men who have owned pets before marrying, not after.
4. Correct. This option provides additional evidence of a causal correlation between pet ownership and the likelihood of divorce.
5. The question concerns men, not their wives.

## 5. The Texas Sharpshooter

The “Texas Sharpshooter” name comes from shooting at a wall and then painting a bullseye afterwards to make it look like a bullseye hit. This is cherry picking a data cluster to suit the argument or finding a pattern to fit a presumption after the fact (another type of biased sampling fallacy). So, you come up with a theory and then scour data to cherry pick to establish a causal relationship. .

This is teleological thinking (that means working backwards from something that happened trying to find its cause). A good example of this is a scandal involving a professor who looked through data to draw conclusions after the fact to make a remarkable conclusion.

For years, Wansink enjoyed a level of prominence that many academics would strive for, his work spawning countless news stories. He published a study showing that people who ate from “bottomless” bowls of soup continue to eat as their bowls are refilled, as a parable about the potential health effects of large portion sizes. Another, with the title “Bad popcorn in big buckets,” similarly warned about the perils of presenting food in big quantities, according to Vox.

Wansink would score data and look for random correlations and then state that it was a causal relationship. If you look at enough correlations, some will just appear out of sheer chance and this doesn’t necessarily mean anything.

## 6. Confounding Factors

A confounding factor (also called “lurking variables”) is an additional factor that may be responsible for a correlation. “Con” is a Latin root for “with,” so “confounding” means literally to “found with.”

Does marriage cause happiness because it is correlated with it?

Or, people who get married are already happy (a confounding factor). Confounding factors weaken the causal relationships of correlations.

Example 1: The Miracle Hospital

A sports injury treatment center in New York has the lowest rate of recovery for sports injuries. A treatment center in rural Pennsylvania has the highest and quickest recovery rate.

If you have just been severely injured while playing softball, should you go to Pennsylvania?

### Explanation Example 1

In this example, it appears pretty obvious that this hospital in New York is bad for your health. So you follow the statistics and go to Pennsylvania, right? The treatment center in New York is an option of last resort for serious sports injury patients like you. The Pennsylvania hospital is so poor that no one with a serious injury ever goes there. The hospital’s patients consist of those with minor injuries who recover quickly. The hidden confounding factor in this argument is that people with more severe injuries are choosing to go to New York, meaning that looking at their injuries is a biased sample.

Example 2: The Secret Conspiracy Against Men

At the Apex Institute of Technology, the school had a much lower acceptance rate for men than for women, and administrators could not determine why since the male applicants had higher SAT scores and grades.

Are the lower admissions rates of men a result of systematic bias?

### Explanation Example 2

Looking at the information, it appears that someone in the admissions department doesn’t like men and has been secretly rejecting their applications.

When looking more carefully at the data, men were much more likely to apply to the highly-competitive engineering program. The result was that men had lower rates of admission overall at Apex Institute of Technology. In non-engineering programs, however, the acceptance rates were identical. So gender played no direct role in admissions rates; the factor was the major chosen by the applicants.

#### Sample Questions

Columnist: Novels published in the past 50 years are far inferior to those published even as recently as 100 years ago. Consider the difference between James Joyce’s Ulysses, from 1922, and the paperbacks you can find featured on the displays at the mega-bookstores. Joyce’s writing is art, these contemporary books are no better than TV sitcoms.

Which of the following points to the most serious logical flaw in the columnist’s argument?

1. The books the author picked from the bookstore displays may not represent the quality of modern writing.
2. The mega-bookstores choose their featured books according to several well-known best-seller lists.
3. Novelists today can rely on their publicists and the Internet to boost the popularity of their novels.
4. There could be criteria other than artistic merit by which to analyze a book’s quality.

### Explanation

(A) You need to find the statement that points to the most serious flaw in the columnist’s argument. This argument, which claims that novels published within the last 50 years are inferior to those published even 100 years ago, relies on a comparison of James Joyce’s Ulysses and the paperbacks featured in the mega-bookstores. It is very possible that those books are not adequately representative of novels written in the two time periods being compared. Choice (A) points out this flaw by suggesting that the columnist’s choice of books is not representative.

Choice (B) offers no evidence either for or against the columnist’s argument. Even if these featured books are best-sellers, this tells you nothing about their quality. All it tells you is that the mega-bookstores feature what the public likes.

Choice (C) supports the columnist’s argument by suggesting that books published today become popular (and publishable, you might infer) mainly through advertising, which could imply that they don’t need to be well-written to be published (or featured in a bookstore).

Choice (D) is incorrect because the author does use terms that address quality (“no better than”). You may infer that what the author means by “inferior” is “less artistic,” so that the argument is merely less convincing than it could be.

Because choice (A) suggests that the columnist’s choice of books is not representative, it is the best answer.

A chemical leak at a local factory contaminated the water at a nearby fishing area. To compensate for the damage they caused to the river, the factory has announced their intention to donate a large sum of money to local environmental conservation efforts.

The factory’s inadequate method of compensation was most likely caused by which of the following errors of reasoning?

1. Mistaking cause for effect
2. Equating a part with the whole
3. Miscalculating a value
4. Ignoring a premise

### Explanation

This question asks you to identify an error in reasoning. The error lies in the method of compensation decided upon by the factory; in donating to the general cause of conservation in the area instead of giving the money directly to the fishing area, they failed to correct the damage they caused to that specific area of land.

Choice B describes this best, as the money will be spent on all of the land in the area instead of the part contaminated by the chemical leak. Choice B is the best answer. Difficulty: Medium/Hard

#### Statistical Reasoning

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4 questions with video explanations

100 seconds per question

Next LSAT: Jun 10/Jun 11