Collider Bias: Why Controlling for the Wrong Variable Destroys Your Analysis
Collider bias shows how conditioning on a shared effect of two variables creates a spurious association that wasn't there before. Here's how it works.
C. Pearson
data science / statistics / probability
Mean Methods.
The mean is lying to you
n = 6 | p < 0.05 | σ ≠ 0
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The Texas Sharpshooter Fallacy: How You Paint a Target Around Your Own Bullet Holes
The Texas Sharpshooter Fallacy explains why finding patterns in data after you've already seen it is a recipe for false discovery.
C. PearsonThe Inspection Paradox: Why the Bus Always Comes Right After You Give Up Waiting
The inspection paradox explains why random intervals feel longer than they are, and why your data keeps lying about averages in ways that are mathematically guaranteed.
C. PearsonSpurious Correlation: Why Your Data Found a Pattern That Doesn't Exist
Spurious correlations aren't just statistical jokes. They're a systematic failure mode that derails real analyses, and your model won't warn you.
C. PearsonLord's Paradox: When the Same Data Gives You the Opposite Answer Depending on How You Look at It
Lord's Paradox shows how two statisticians can analyze the same dataset and reach contradictory conclusions, both correctly. Here's why that should unsettle you.
C. PearsonMeasurement Invariance: Why You Can't Compare Groups Until You Prove Your Scale Works the Same Way
Measurement invariance is the silent assumption breaking your group comparisons. Here's what it means, why it fails, and how to test for it.
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