Slippery Slope

Suppose someone claims that a first step (in a chain of causes and effects, or a chain of reasoning) will probably lead to a second step that in turn will probably lead to another step and so on until a final step ends in trouble. If the likelihood of the trouble occurring is exaggerated, the slippery slope fallacy is committed.

Example:

Mom: Those look like bags under your eyes. Are you getting enough sleep?

Jeff: I had a test and stayed up late studying.

Mom: You didn't take any drugs, did you?

Jeff: Just caffeine in my coffee, like I always do.

Mom: Jeff! You know what happens when people take drugs! Pretty soon the caffeine won't be strong enough. Then you will take something stronger, maybe someone's diet pill. Then, something even stronger. Eventually, you will be doing cocaine. Then you will be a crack addict! So, don't drink that coffee.

The form of a slippery slope fallacy looks like this:

A leads to B.
B leads to C.
C leads to D.
...
Z leads to HELL.
We don't want to go to HELL.
So, don't take that first step A.

Think of the sequence A, B, C, D, ..., Z as a sequence of closely stacked dominoes. The key claim in the fallacy is that pushing over the first one will start a chain reaction of falling dominoes, each one triggering the next. But the analyst asks how likely is it really that pushing the first will lead to the fall of the last? For example, if A leads to B with a probability of 80 percent, and B leads to C with a probability of 80 percent, and C leads to D with a probability of 80 percent, is it likely that A will eventually lead to D? No, not at all; there is about a 50- 50 chance. The proper analysis of a slippery slope argument depends on sensitivity to such probabilistic calculations. Regarding terminology, if the chain of reasoning A, B, C, D, ..., Z is about causes, then the fallacy is called the Domino Fallacy.