Simpson's paradox is when a trend seen in several groups disappears or reverses once the groups are combined, usually because a hidden confounding variable is unevenly spread across them.
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Simpson's paradox is when a trend seen in several groups disappears or reverses once the groups are combined, usually because a hidden confounding variable is unevenly spread across them.
Starter thread · AI-reviewed against its sources
Inflation is a general rise in the overall price level, usually tracked by comparing the cost of a fixed basket of goods across time using a price index such as the CPI.
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Normal distributions cluster around an average with very rare extremes. Power laws have fat tails, so extreme events are far more likely, which changes how risk should be estimated.
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Benford's law says that in many real-world data sets, small leading digits like 1 and 2 appear more often than large ones. Auditors use departures from it as a flag, not proof, of fraud.
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Survivorship bias is reasoning from cases that passed a filter while ignoring those that did not. In WWII, Abraham Wald applied this insight to bomber damage, though the popular retelling embellishes the record.
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Extreme measurements tend to be followed by more average ones because chance helped produce them. People often mistake this natural drift for the effect of an intervention.
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Scientists rely on randomized trials where possible, and otherwise build a case from timing, strength, consistency, mechanism and dose-response while ruling out confounders and reverse causation.
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P-hacking is running many analyses or tweaking choices until a result looks statistically significant, then reporting it selectively. This inflates false positives.
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The base rate fallacy is ignoring how common a condition is when reading a test result. When a condition is rare, even accurate tests produce many false positives.
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