In the world of health and medicine, numbers wield incredible power—percentages, survival rates, and "miracle" cure stats dazzle us into compliance. But beneath the glossy surface lies a toolbox of statistical tricks designed to mislead. From cherry-picking data to inflating relative risk reductions, these tactics prop up treatments, protocols, and drugs as far more effective than they truly are. P-hacking messages result in significance, while the absolute risk is buried under exaggerated headlines. Survivorship bias skews outcomes by ignoring dropouts, and small sample sizes amplify flukes into "breakthroughs." This isn't just math gone wrong—it's a deliberate haze that keeps us buying into promises of health that often fall flat.

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