7. Slippery Slope

7. Slippery Slope

The last “content fallacy” that we’re going to look at is “slippery slope”.

Here’s a pretty extreme example of a slippery slope fallacy.

A high school kid’s mom insists that she study on Saturdays. Why? Because if she DOESN’T study on Saturdays then her grades will suffer and she won’t graduate high school with honors, and if she doesn’t graduate with honors then she won’t be able to get into the university of her choice, and ... well, the rest isn’t clear, but the result of all this is that she’ll end up flipping burgers for the rest of her life, and surely she doesn’t want THAT, so she’d better darn well get serious and study!

I’ve actually heard a version of this discussion between two wealthy mothers who were talking about which preschool to send their kids to. The gist was that if they didn’t get their kid into a prestigious preschool then they’d be disadvantaged from that point forward in ways that could ultimately threaten their future life prospects, so this was not a decision to be taken lightly!

I did not envy those kids.

Here’s the schematic form of a slippery slope argument.

1. If A then B
2. If B then C
3. If C then D
4. not-D
Therefore, not-A

It’s a series of connected conditional claims, to the effect that if you assume that A is true or allow A to occur, then B will follow, and if B follows then C will follow, and if C follows then D will follow. But D is something nasty that we all want to avoid, so the conclusion is that if we want to avoid D, we need to reject A, or not allow A to happen.

Note that, as stated, the logic of this argument is fine. In fact, this is a valid argument form that we’ve seen before, we’ve called it “hypothetical syllogism” or “reasoning-in-a-chain” with conditionals.

Slippery slopes are fallacious only if the premises are false or implausible. Everything turns on whether these conditional relationships hold. Sometimes they do, and if they do, it’s not a fallacy. But very often then don’t, and when they don’t we’ve got a slippery slope fallacy.

An Important Caveat

Now, there’s a caveat to this way of analyzing slippery slopes. It’s usually the case that slippery slope arguments aren’t intended to be valid. That is, they’re not intended to establish that the dreaded consequence will follow with absolute certainty. Usually the intent is to argue that if you assume A, then D is very likely to follow, so what’s being aimed for is really a strong argument.

And that means we shouldn’t really be reading the conditional claims as strict conditionals, with every link in the chain following with absolute necessity. We should be asking ourselves, how likely is it that D will follow, if A occurs? If it’s very likely, then the logic is strong, if not then it’s weak. So in a sense we’re evaluating the logic of the argument, but it turns out that in cases like this, the strength of the logic turns on the content of the premises, so in the end we are evaluating the plausibility of premises, which makes this a content fallacy, and not a logical or formal fallacy.

For our example the chain of inferences looks like this:

Doesn’t study on Saturdays →
Doesn’t graduate high school with honors →
Doesn’t get into a top university →
Winds up working in a fast food restaurant (or similar “working class” career”)

This argument is obviously bad, at every stage of the reasoning.

It’s possible that not studying on Saturdays could make a difference to whether the student gets on the honor roll, but there’s no evidence to suggest that this is likely.

Yes, if you’re not on the honor roll then maybe this will affect your chances of getting into a top university, but without specifying what counts as a top university, and what other factors may or may not be operating (like, for example, whether the student is a minority or an athlete and might be eligible for non-academic scholarships of various kinds), then it’s impossible to assess the chances of this case.

The last move, from failing to get into a top university to flipping burgers for a living, is obviously the weakest link in the chain, this is just wildly pessimistic speculation with nothing to support it.

So each link in the chain is weak, and the chain as a whole simply compounds these weaknesses.

By saying this we’re saying that premises 1, 2 and 3 are not plausible, and so the inference from A to D is not plausible. We have no reason to think that this slope is “slippery”.

* * *

Now, there’s another obvious way that one can attack a slippery slope argument. You might be willing to grant that the slope is slippery, but deny that what awaits at the bottom of the slope is really all that bad.

This would be to challenge premise 4, “not-D”. “not-D” says that D is objectionable in some way, that we don’t want to accept D. But this might be open to debate. If what awaits at the bottom of the slope is “and then you die a painful death”, or “and then all our civil rights are taken away”, then sure, just about everyone is going to agree that that’s a bad outcome.

But it’s not as obvious that everyone will find flipping burgers objectionable, or whatever this notion stands for — working in the service industry, or working in a low-paying job, or whatever.

What’s important in evaluating a slippery slope argument is that the intended audience of the argument finds the bottom of the slope objectionable. So this is another way to criticize a slippery slope argument — by arguing that the outcome of this chain of events really isn’t as objectionable as the arguer would like you to think.


So, just to summarize what we’ve said so far, there are two ways of challenging a slippery slope argument.

  1. Challenge the strength of the conditional relationships that the argument relies on. When people say that a slippery slope argument is fallacious, they usually mean that this chain of inferences is weak.
  2. Challenge the “objectionableness” of whatever lies at the end of the chain. If it’s not obvious to the intended audience that this is actually a bad thing, then the argument will fail to persuade, regardless of how slippery the slope may be.

Some Comments on Assessing the Plausibility of Conditional Claims

Before wrapping up, I’d like to make a few points about assessing the plausibility of conditional chains. Fallacious slippery slope arguments often succeed at persuading their audience because people misjudge the strength of the chain of inferences. They’re prone to thinking that the chain is stronger than it actually is.

It’s important to realize two things. First, a chain of conditional inferences is only as strong as its weakest link. The weakest conditional claim, the one that is least likely to be true, is the one that sets the upper bound on the strength of the chain as a whole. So even if some of the inferences in the chain are plausible, the chain itself is only as strong as the weakest inference.

Second, weaknesses in the links have a compounding effect, so the strength of the whole chain is almost always much weaker than the weakest link. To see why this is so, you can think of conditional claims as probabilistic inferences — If A is true, then B follows with some probability, and this probability is usually less than 1, or less than 100%.

So the probability of D following from A, the probability of the whole inference, is actually a multiplicative product of the probabilities of each of the individual links.

The odds of a coin landing heads on a single toss is 1/2, or 50%. The odds of a coin landing heads twice in a row is 1/2 times 1/2, or 1/4, which is 25%. Conditional inferences compound in a similar way.

So, if the odds for each link in the chain were, let’s say, 90%, then the odds of the whole chain being true, of D actually following from A, would only be 0.73, or 73%, and this number will go down further with each additional link in the chain.

People, in general, are very bad at estimating compound probabilities, and we’ll tend to overestimate them.

What’s the estimate if the one of the links is weaker than the rest, say, 0.6, or 60%. The probability of D following from A actually drops below 50%, a very weak inference, but very few people will read the probabilities this way. Their attention will focus on the highly probable bits of the story and their estimate of the overall odds will be anchored to these bits, especially if they’re either at the very beginning or at the very end of the chain, since these make the biggest impression.

So, human beings in general are quite vulnerable to slippery slope reasoning, and knowing these facts should motivate you to be more critical when you encounter these kinds of arguments.