Lesson Summary
All generalizations are false, including this one.
Mark Twain, American author and humorist (1835–1910)
As with deductive reasoning, an inductive argument that has poor reasoning to support its conclusion is a fallacy. An inductive fallacy has either two premises that don't support its conclusion, or a conclusion that doesn't fit the premises. In this lesson, you'll learn how to spot some common logical fallacies so you're not taken in by their faulty logic.
The conclusion drawn in an inductive argument is only as good as the quantity and quality of its premises. There are many ways to create a strong inductive argument, and just as many ways to create a weak one. It's important to understand the different logical fallacies that can make an argument weak, so you'll know one when you see or hear them, and avoid using them yourself.
Chicken-and-Egg Fallacy
"Which came first, the chicken or the egg?" That age-old question is used to classify problems for which there are no easy answers. What does that have to do with inductive arguments? If you're creating a logical argument, using two events, you can't take it for granted that because the two things regularly happen together, one causes the other. That's the chicken-and-egg fallacy. It follows this general form:
- A and B regularly occur together.
- Therefore, A is the cause of B.
This fallacy assumes that one event must cause another just because the events occur together. The assumption is based on inadequate justification; there is not enough evidence to draw the causal conclusion.
A common example of chicken-and-egg fallacy is the relationship between TV/movie violence and real-life violent behavior. Many people believe that a person's violent behavior is the result of watching TV/ movie violence. Other people contend that if someone is a violent person, he or she will create, watch, and enjoy violent programming. So, does TV/movie violence cause real-life violence, or vice versa? Or is there no causal relationship between the two? The simple fact is that some people are violent, and some entertaining TV shows and movies contain violence. But there is not enough evidence to assert a connection, since many people watch violent TV shows/movies and never become violent themselves.
How can you avoid falling into the chicken-andegg fallacy? Since it means drawing a conclusion without enough evidence presented to show any cause-and-effect relationship, you can avoid it by paying careful attention to the sequence of events. If A happens after B, A can't possibly cause B. Ask yourself if there's anything else that could have been the cause. Think about the evidence presented. Is it enough to draw the conclusion?
Examples
- Many smokers have lung cancer. Lung cancer causes people to smoke.
- You can't get a job unless you have experience. You can't get experience unless someone gives you a job!
- Last night I had a fever. This morning, I have a cold and a fever. The fever caused the cold.
Hasty Generalization
In this fallacy, there's not enough data or cases to warrant a generalization, For example, a waitress complains, "Those Southerners left me a lousy tip. All Southerners are cheap!" She has made a generalization about tens of millions of people based on an experience with a few of them. A hasty generalization like hers takes this form:
- A very small sample A is taken from population B.
- Generalization C is made about population B based on sample A.
There are two common reasons for hasty generalizations. One is because of bias or prejudice. For instance, a sexist person could conclude that all women are bad drivers because he had an accident with one. Hasty generalizations are also often made because of negligence or laziness. It is not always easy to get a large enough sample to draw a reasonable conclusion. But if you can't get the right sample, do not make the generalization. Better yet, make an attempt to add to your sample size. Improve your argument with better evidence.
How do you know when your sample is large enough? There is no one rule that applies to every type of sample, so you will need to use the "practicality and reasonability" test. What is the largest sample you can gather that makes sense, practically? Will it be large enough so that you can reasonably make a generalization about it? Reread the section on statistics in Lesson 10 to refresh your memory about the problems that can occur when taking a sample, and how those problems can be recognized and/or avoided.
Try to avoid jumping to conclusions, and learn to spot when others have done so in their arguments. If a generalization is the result of prior opinions about people in question, the bias needs to be removed and the generalization rethought, based on real information. You also need to take the time to get a large enough sample so that a generalization drawn from that sample makes sense. To generalize about a large group of people, you need to find out about many more of them than when generalizing about a very small group.
Examples
- I asked eight of my coworkers what they thought of the new manufacturing rules, and they all thought they are a bad idea. The new rules are generally unpopular.
- That new police drama is a really well done show. All police dramas are great shows.
- Omar threw the ball from left field to the second baseman, and he made an incredible double play. Whenever Omar gets the ball, he should throw it to the second baseman.
Tip
You can avoid erroneous generalizations by being specific. People should know exactly what your message is.
Composition
This fallacy occurs when the qualities of the parts of a whole are assumed to also be the qualities of the whole. It is a fallacy because there is no justification for making this assumption. For example, someone might argue that because every individual part of a large machine is lightweight, the machine itself is lightweight.
This argument is fallacious; you cannot conclude that because the parts of a whole have (or lack) certain qualities, the whole that they are parts of has those qualities. Let's look at another example. A girl's mother tells her, "You love meatloaf, applesauce, ice cream, and pickles. So, you will love what we're having for dinner tonight! I made a meatloaf, applesauce, ice cream, and pickle casserole." This is an example of the fallacy of composition because, while the girl loves all of those foods individually, one cannot reasonably conclude that she will love them when they are put together as a casserole (a whole made of the likeable parts is not necessarily likeable).
Sometimes an argument that states the properties of the parts are also the properties of the whole can be a strong one. For example, if every piece of a table is wood, there's no fallacy in the conclusion that the whole table is wood. To determine whether a statement is fallacious or not, you need to determine if there's a justification for the inference from parts to whole.
Post Hoc, Ergo Propter Hoc
When you read about the chicken-and-egg fallacy earlier in this lesson, you noted that just because one event precedes another doesn't mean that the first caused the second. To assume it does is another illogical fallacy known as post hoc, ergo propter hoc, Latin for, "after this, therefore because of this." Often shortened to just post hoc and sometimes referred to as false cause, this fallacy follows the pattern:
- Event A precedes event B.
- Event A caused event B.
But to make a strong causal argument, you must account for all relevant details. For example, every time Ahmed tries to open a video program on his computer, it crashes. He concludes that the program is causing the computer to crash. However, computers are complex machines, and there could be many other causes for the crashes. The fact that the opening of one program always precedes the crash is a good possibility for cause, but it cannot be maintained as the one and only cause until a stronger link is made. To avoid the post hoc fallacy, he would need to show that all of the many other possibilities for the cause of the crashing have been evaluated and proven to be irrelevant.
Superstitions are another example of post hoc fallacies. Some superstitions are widely held, such as "if you break a mirror, you will have seven years of bad luck." Others are more personal, such as the wearing of a lucky article of clothing. However, all of them are post hoc fallacies because they do not account for the many other possible causes of the effect. Bad luck could happen to someone who breaks a mirror, but bad things also happen to those who do not. In these cases of superstitions, the real cause is usually coincidence.
How can you strengthen an argument and keep it from becoming an example of the post hoc fallacy? First, show that the effect would not occur if the cause did not occur. For example, if I don't strike the match, it will not catch on fire. Second, be certain there is no other cause that could result in the effect. Are there any sources of flame near the match? Do matches spontaneously catch fire? Is there anything else that could cause it to catch fire? If the answer is no, then there is no post hoc fallacy.
Examples
- I took three echinacea tablets every day when my cold started. Within a week, my cold was gone, thanks to the echinacea.
- I wanted to do well on the test, so I used my lucky pen. It worked again! I got an A.
- Last night I had a dream that there was a car accident in my town. When I read the paper this morning, I found out a car accident did happen last night. My dreams predict the future.
Tip
If you say that A causes B, have something more to say about how other than just that it came first! You might want to use data and statistics to make your point.
In Short
Inductive reasoning is used all the time to make generalizations from specifics. But it can be misused to create arguments for things such as racial prejudice and superstitions. These weak arguments involve fallacies such as hasty conclusions, chicken and egg, and composition (making a conclusion about a whole based on the qualities of its parts). Learning how to recognize such faulty reasoning will help you to avoid being tricked by it, and also help you avoid making such mistakes in your arguments.
Skill Building Until Next Time
- Read the science section of your newspaper or a science article in a magazine and find an example of inductive reasoning. Check for fallacies. If none exist, come up with a way to apply one of the fallacies in this lesson to the example.
- Remember that in order to determine cause, you must have enough evidence to support the conclusion. The next time you are blamed for something or hear someone blaming another person, think about this: Do they have strong premises on which to base their conclusion? Who or what could have been the real cause?
Exercises for this concept can be found at Inductive Fallacy Practice Exercises.
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