Affirming the consequent

Another common fallacy is called ‘affirming the consequent’. This rather quaint phrase refers to a type of fallacy where one of the initial premises is not enough to support the conclusion. Consider the following argument:

Premise: Tigers have fur
Premise: Tigers are mammals
Premise: Humans are mammals

Conclusion: Humans have fur

The mistake in reasoning here occurs in presuming that because tigers are mammals and tigers have fur, all mammals have fur. Essentially whilst all the premises are true, there has been a incorrect jump between the premises and conclusions.

Additionally, there is the argument from ignorance. The argument from ignorance is a fallacy that occurs when someone uses a lack of proof or evidence or an inability to prove otherwise, as proof for the existence or truth of a point. For example, we might not be able to disprove the existence of ghosts, but this doesn’t lend credence to their existence either.

Or consider an alternative that is less common, but shares the same logical structure; we cannot disprove the existence of invisible and unobservable space unicorns. Here, our inability to disprove doesn’t in any way lead to any credibility to the existence of unobservable space unicorns – for something to be warranted with possible belief, it’s existence must be provable or credited in some way, not just an inability to be discredited.

On top of this, another fallacy is over-generalization. In this fallacy, someone uses a narrow example or experience to generalize to a whole group. Someone who bases their opinion purely on anecdotes and personal experience is susceptible to this type of fallacy – for example, it could lead to the belief that all blonde people are mistrust-worthy due to bad experiences with blond people previously. In the real-world over-generalization is often a cause for racism, sexism and xenophobia.

Finally, the last common fallacy that is worth paying attention to is post hoc ergo propter hoc, which is often paraphrased as ‘post hoc’ or simply correlation vs. correlation. In statistics a correlation is when two variables display a pattern together. Causation, however, is when two variables display a relationship and one variable causes a change in the other. For example, we might suppose that there is a causation between how hot the weather is and how many people go to the beach, as we understand that warmer weather encourages people to take to the sands. In this example there is an inherent relation between the two variables we are looking at, such that one causes the other.

However, many variables might correlate and show a pattern without a meaningful relationship. A study that collects data might reveal that people who like horror movies are also more likely to listen to jazz music. It’s probably unlikely that there is a causal relationship between horror movies and jazz music however; if we made a group of people watch horror movies, we wouldn’t expect them to start suddenly liking jazz music more.

Correlations are often meaningless or they reveal another variable or pattern that isn’t being paid attention to. Perhaps for example, older people are more likely to listen to jazz and older people tend to like horror movies more. If this is the case, the correlation isn’t revealing a relationship between jazz music and horror movies, but a relationship between age & jazz music and between age & horror movies. The fact that we noticed a pattern between horror movies & jazz music was misleading and coincidental.

There are many, many more logical fallacies – too many to fully disclose here. However, you should learn to recognize them. Every time you feel that there is something wrong with the validity of someone’s argument you can probably analyze their argument and pick up on a common fallacy, which will become easier to recognize in the future.


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