Last year, a software company held a contest to generate ideas for their new logo. According to the rules, everyone w...

Emil-Kunkin January 17, 2022

Hi @connordelacruz, this is a generally tricky topic. I like to think about it in the following way. Sufficient/necessary relationships are those that give a general rule stating that one thing always leads to a certain outcome, or that lack of one thing always leads to the lack of a certain outcome. In other words, if one thing guarantees another, it is sufficient for that outcome, and if the lack of one thing guarantees the lack of the other, then the thing would have been necessary.

A causal relationship is quite similar, but does not involve quite as much certainty. If we say that lack of vitamin C causes a disease called scurvy, we may not be certain if this is truly sufficient, and we have no idea if there are other things that can cause scurvy. However, it is a bit less challenging to recognize causation/correlation flaws than it is to fully understand causal relationships. This sort of flaw comes about because an author overgeneralizes about one thing causing another. This could be because the author used a sample size that was too small or unrepresentative, because the correlation was caused by some third thing, or because the author mistook the direction of causation. That is, Assuming X caused Y when really Y caused X. In every case of a causation correlation flaw, the author will be generalizing from a statistical relationship.

In this case we are given a set of facts, and told that joining the competition was sufficient to guarantee getting a shirt. The author does not cite any correlation, only a specific example where one person has a shirt.

I would recommend taking a look at some office hours about both sufficient and necessary and causation flaws, as those can also help you to recognize them.