Nothing is so difficult as not deceiving oneself.
Ludwig Wittgenstein
The fallacy of the false cause is an argument that assumes there is a causal relation between two events, without providing supporting evidence. Events often occur simultaneously or consecutively, however, this does not mean that they are necessarily related. For example:
1. The Chinese restaurant was robbed Friday night and again Saturday night. It must have been robbed by the same perpetrator.
2. Floods and tornadoes devastated the small town of Green Hills. It is clear that a Divine Being was punishing the town for its non-religious practices.
In the first example, the assumption is made that due to the same location of the robberies, the cause of both crimes must be the same. Without further supporting information, however, it is impossible to know if the same criminal was responsible for both events. In the second example, the assumption is made that two consecutive events (the town’s non-religious practices and the floods and tornadoes) are causally related. But, like the first example, a causal relationship cannot be established.
All causal fallacies rest on a misunderstanding of cause and effect. The clearest examples of these are the fallacy of the false cause (above), the fallacy of the single cause, post hoc ergo propter hoc, and the slippery slope fallacy. These last three arguments will be explained next, followed by the gambler’s fallacy and the appeal to consequences.
This fallacy is very similar to the fallacy of the false cause, described above. The fallacy of the single cause, however, states that an event has only one root or reason, when in fact there may be multiple causes. In contrast, the false cause fallacy is erroneous in its assumption of the cause in general. Consider this example of the fallacy of the single cause:
1. Mr. Rivers gunned down three innocent people because he was on drugs.
According to this claim, the cause for the shootings was drugs. However, situations like this are usually multifaceted and complicated. It is highly doubtful that drugs were the sole cause of Mr. Rivers’ crime; drugs were most likely only one causal component of the complex event. Drugs combined with anger, fear, exhaustion, desperation, and/or ignorance might be a more accurate explanation. In any case, we simply do not know all of the causal factors involved in this case, so we need to refrain from assuming only one cause. Consider the following example:
2. The terrorists attacked us because they hate us.
In this argument, the stated conclusion is clearly an oversimplification. Hatred may or may not be one causal factor among several, but many other explanations should be considered when discussing terrorism. We must always remember that events often have more than one causal component.
Another fallacy of causation, the name of this fallacy translates from the Latin as “after this, therefore because of this.” It is also known simply as the post hoc fallacy. This fallacy occurs when one presupposes that two consecutive but independently occurring events are causally related. In other words, one assumes that because one event happened after another event, the first event must have caused the second. For example:
1. Smith was elected Governor in May. In June, crime rates skyrocketed. Clearly, Smith must have caused crime to increase.
2. I lost my wallet the afternoon after I kicked my neighbor’s cat. Therefore, my unethical act caused me somehow to lose my wallet.
3. Teen pregnancies have decreased since the creation of the Internet. So, it is clear that the Internet prevents teen pregnancies.
Like the fallacy of the false cause and the fallacy of the single cause, the post hoc fallacy rests on a misunderstanding of cause and effect. Remember, simply because two events occur sequentially, we cannot infer that the former event necessarily caused the latter. We must be careful not to project a causal relationship onto unrelated events.
This fallacy occurs when one erroneously argues that if a certain act or event takes place, it will necessarily lead to a chain of events, ending in a dramatic or disastrous final result. The slippery slope is a fallacy precisely because we can never know if a whole series of events and/or a certain result is determined to follow one event or action in particular. Usually, but not always, the slippery slope argument is used as a fear tactic. Consider these examples:
1. You should not major in philosophy. If you do, you’ll never get a good job, you’ll lose friends, and no one will want to marry you. If you major in philosophy, you’ll end up penniless and alone.
2. Playing the lottery will lead to gambling addiction, which means losing your house, your spouse, and everything that is precious to you. You’ll find yourself homeless on the streets of Las Vegas, begging for change.
3. Marijuana is the gateway drug. Using it will lead to cocaine, heroine and crack use. Smoke a joint, and before you know it, you’ll find yourself with an unquenchable desire for crack!
As we can see from these examples, the slippery slope fallacy claims to know the precise causal links and result following a certain event or action. Furthermore, these events usually end in a negative result.
Yet the slippery slope fallacy can also argue for a chain of causal events that necessarily lead to a positive result. For example:
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With this example, it becomes clear that the slippery slope can end in one of two extreme places: positive or negative. It should be noted, however, that the slippery slope always leads to a dramatic result, and always implies that the series of events leading to that result necessarily followed from the first action or event.
Sometimes, a slippery slope fallacy does not include or explain the linking events between the first and final ones. For example:
5. If Barbara rents her house to college students, the property value of the entire neighborhood will drop.
6. If we allow the government to listen in on our phone calls, then before we know it, even our sex lives will be monitored!
These two examples are slightly different versions of the slippery slope fallacy. They both follow the same pattern as presented above, but with the implicit assumption of negative causal events, rather than a clear presentation of a chain of events.
This fallacy of causation occurs when one mistakenly believes that an independently occurring event, such as a coin toss, has a causal effect on a separate event, such as a second coin toss. It is named the gambler’s fallacy because this type of reasoning is commonly (but not exclusively) found in casinos. For example, if a person wins three hands of cards in a row, he or she might think, “I am on a roll! I am sure to continue winning!” However, there is not a causal relationship between previous card games and later card games—they are separate and non-causally related. Consider these examples:
1. I bought three lottery tickets, and I won money on the first two! I am sure to win on the third ticket, too!
2. The Raiders won the last two Monday nights. We can safely assume that they will win next Monday night as well.
As seen in the above examples, the gambler’s fallacy is often used to predict that an upcoming event will be the same as a previous event. However, it is also commonly used to predict the opposite result. Here are some examples:
3. Our first two children were boys. This means that our next child will certainly be a girl.
4. I am sure that the Lakers will win their next game. How can I know this?
Because they have lost five games in a row and they have to break their losing streak soon.
5. The last three guys I dated were losers. My next boyfriend can’t possibly be a loser.
The fact that this fallacy can be used to predict opposite results makes its flawed nature glaringly evident. Yet the central issue here is this: the gambler’s fallacy, like other causal fallacies, confuses the notion of cause and effect. One must remember that there are two types of events: causal and non-causal. A causal event is one in which an action or event necessarily causes a following event, such as a pool cue hitting a billiard ball, causing the ball to roll. A non-causal event is one that has no necessary or predictable following event, such as a winning raffle ticket foretelling future wins or losses.
The appeal to consequences is a causal fallacy stating that the truth of a proposition is determined by the negative or positive consequences of that proposition. In other words, this fallacy is committed when one argues that a statement must be true or false, depending on whether the consequences are perceived to be good or bad. For example:
1. Hal: I know that God does not exist.
Susan: What evidence do you have to support this claim?
Hal: Well, just look at all of the harm that has been done in the name of God!
In this example, Hal does not provide solid premises to support his claim. Instead he points out that belief in God has had certain negative consequences. However, simply because harm has been done in the name of God, this does not mean that God does not exist. Likewise, simply because some churches and other religious institutions feed, clothe, and provide shelter for the poor, this does not necessarily mean that their religious beliefs are grounded in reality. Consider these examples:
2. Evolutionary theory is correct for many reasons. The strongest evidence for evolution lies in the fact that people who accept this theory are more civilized than those who do not.
3. I do not believe that the United States Military is presently killing innocent civilians in Iraq and Afghanistan. The thought of this is so disturbing and depressing that it cannot possibly be true.
In each of these examples the arguer appeals to the consequences of a particular belief in order to accept or dismiss it. The problem with this type of reasoning is that there is not a causal relationship between the entailments and the truth of a proposition. Consequences often have nothing to do with the truth or falsity of their cause.