Bias doesn’t look like a mistake — and that’s why it’s so dangerous.

In this episode, we explain how bias enters statistical studies, why bias is fundamentally different from random error, and why it is often difficult to detect and remove. You’ll learn how bias can be built into data collection, study design, and real-world systems, even when advanced statistical or machine learning methods are used.

This episode introduces common types of bias encountered in public health, biostatistics, and data science, and explains how biostatisticians work to identify, assess, and reduce bias while acknowledging its limitations.

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