index f4a50fc..14f615b 100644
@@ -57,7 +57,7 @@ humans are terrible at assessing risk intuitively:
- we treat very small probabilities as zero (until it happens to us)
- we treat very small probabilities as large when the outcome is vivid (nuclear meltdown)
-the fix isn't to "stop being biased" — it's to do the math. [estimation](/wiki/immediate/patterns-and-estimation) skills help here: if you can get the order of magnitude right, you're already ahead of most people's intuitions.
+the fix isn't to "stop being biased" — it's to do the math. [[patterns-and-estimation|estimation]] skills help here: if you can get the order of magnitude right, you're already ahead of most people's intuitions.
## independence and conditional probability
@@ -65,6 +65,6 @@ two dice rolls are independent — the first doesn't affect the second. but many
the gambler's fallacy — "I've flipped 5 heads in a row, so tails is due" — is an error about independence. the coin doesn't remember its history. but in many real situations, history *does* matter: a basketball player who's made their last 5 shots might genuinely have a higher probability of making the next one (hot hand is real, it turns out, though weaker than people think).
-understanding when events are truly independent vs when they're correlated is one of the most practically important probabilistic skills. [engineering and modeling](/wiki/stem/engineering-and-modeling) relies heavily on getting these dependencies right — in monte carlo simulation, assuming independence when events are correlated gives wildly wrong results.
+understanding when events are truly independent vs when they're correlated is one of the most practically important probabilistic skills. [[engineering-and-modeling|engineering and modeling]] relies heavily on getting these dependencies right — in monte carlo simulation, assuming independence when events are correlated gives wildly wrong results.
-when probability moves from discrete events (coin flips, dice) to continuous distributions (heights, temperatures, stock prices), you need [[structural/calculus-as-thinking|calculus]] — continuous probability is built on integration, and concepts like probability density functions only make sense through the lens of limits and infinitesimals. the [[immediate/counting-and-measurement|measurement]] of probability itself raises deep questions about what we're actually quantifying.
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+when probability moves from discrete events (coin flips, dice) to continuous distributions (heights, temperatures, stock prices), you need [[calculus-as-thinking|calculus]] — continuous probability is built on integration, and concepts like probability density functions only make sense through the lens of limits and infinitesimals. the [[counting-and-measurement|measurement]] of probability itself raises deep questions about what we're actually quantifying.
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