One thing that sets me apart is my background in mathematics.
I don’t just read studies, I design them, analyze them, and interpret the results myself. In modern medicine, too many physicians rely on reports from other people instead of critically evaluating the data firsthand. I witnessed this firsthand and it led to a cheating scandal.
A 2016 study published in JAMA found that many physicians struggle with even basic statistical concepts like p-values, confidence intervals, and absolute vs. relative risk reduction—all critical for understanding whether a treatment or intervention is truly effective.
In clinical and epidemiological studies statistical models are applied to the results. This is because the studies have many co-variables which cannot, with our current limited understanding, be accounted for. As a result, researchers can choose models favorable to their bias.
For example, clinical trials with positive outcomes are more likely to be published and cited. Commercial goals can be realized.
However, these models are often simply mathematical abstractions which don’t represent reality but instead serve the author’s and their sponsor’s interests. A basic understanding of probability and statistics will serve YOU so you’ll never be misled again.
Without this knowledge, doctors are left to blindly trust external statisticians or pharmaceutical-backed summaries, rather than making independent, data-driven decisions for their patients.
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