A point about FAANG points April 14, 2024 by Eduardo Bellani

As a technologist I often hear variations of the following phrase in my industry:

Do it because some FAANG(Fernando 2023) company did it.

The structure of this argument is usually like this:

1. Technique or process X is great/bad,
2. Company C does it like this,
3. C is financially successful and famous,
4. Therefore, you should do the same X as C does.

This is a mixture of the fallacies of selection bias, appeal to authority and false cause. Here are their definitions and some context-sensitive examples¹:

Selection bias

This is a bias introduced by sampling in a way that is not representative of the population in question.

We should only look at what FAANG companies do (and ignore the ones that
did the same and went bankrupt).

Appeal to authority

You appeal to authority if you back up your reasoning by saying that it is supported by what some authority says on the subject.

However, appealing to authority as a reason to believe something is fallacious whenever the authority appealed to is not really an authority in this particular subject, when the authority cannot be trusted to tell the truth, when authorities disagree on this subject (except for the occasional lone wolf), when the reasoner misquotes the authority, and so forth.

We should start using managed services because AWS tells us to do so.

False cause

Improperly concluding that one thing is a cause of another. Its four principal kinds are the Post Hoc Fallacy, the Fallacy of Cum Hoc, Ergo Propter Hoc, the Regression Fallacy, and the Fallacy of Reversing Causation.

Post hoc

Suppose we notice that an event of kind A is followed in time by an event of kind B, and then hastily leap to the conclusion that A caused B. If so, our reasoning contains the Post Hoc Fallacy

After Facebook build their system with PHP, they became hugely successful.

Cum hoc

Latin for “with this, therefore because of this.” This is a False Cause Fallacy that doesn’t depend on time order (as does the Post hoc fallacy), but on any other chance correlation of the supposed cause being in the presence of the supposed effect.

Google uses lots of microservices and Kubernetes.

Reversing causation

Drawing an improper conclusion about causation due to a causal assumption that reverses cause and effect.

Microsoft and Google both are huge companies and have R&D centers. We
need to have a R&D center to become a huge company

Conclusion

Do pay attention to successful companies, but only when it is valid to do so. Having a great business model and timing can allow a company to survive very bad mistakes (such as Google firing all their project managers once(Editors 2017)).

References