Fallacies: M (Part 2 of 3)

Core idea

The middle run of M entries gathers misconceptions about words, numbers, bodies and animals. "Marmalade" is wrongly traced to a princess's illness when it simply means "made of quinces." Mathematics, supposedly the realm of certainty, turns out to have whole books of fallacies — and Gödel proved that not even arithmetic can prove all its own truths. Mermaids are sailors' glimpses of seacows. The folklore that bears are drawn to menstruating women collapses under a careful study of decades of actual bear attacks.

The unifying thread is the false connection. A girl bleeds; a bear attacks; the mind links them. Milk turns sour; a thunderstorm rolls in; the mind links them too — when in fact the summer heat causes both. The topic trains the reader to pause before any "A goes with B, so A causes B," and ask whether a hidden third factor is doing the real work.

Why it matters

The menstruation entries show how a fallacy can be cruel. Calling women "unclean," barring them from temples, blaming sterile crops on their presence — these are not quaint errors but real harm built on a non-existent causal link. Debunking the bear myth is a small act of dignity: the data show male visitors were attacked far more often, and the link to menstruation simply is not there.

The mathematical entries matter for the opposite reason — they puncture overconfidence. People reach for "mathematically certain" as the gold standard of truth, yet Gödel showed that even a consistent formal system contains true statements it cannot prove. And the "advances in medicine are made by doctors" entry quietly corrects who actually does discovery: the laboratory researchers, not the bedside clinician.

Key takeaways

Mental model

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Practical application

The reusable habit is the third-variable check. Whenever you meet "A happens with B," resist the jump to "A causes B" and ask what else might cause both. Milk and storms look linked only until you notice the sultry weather behind each. The skeptic's question is not "are these correlated?" but "what is the common cause I have not named?"

Example

A team notices that their busiest support days are also their highest-bug days, and concludes that heavy traffic is breaking the software. Apply the M topic's third-variable check. Traffic and bugs are correlated — but what could cause both at once? A new feature launch drives traffic up and introduces fresh bugs; the launch is the hidden common cause. Treating traffic as the culprit would lead the team to throttle users while the real source keeps shipping defects. False-cause fallacies survive because pairing two visible events into a story is far easier than hunting for the invisible factor behind them.

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