Busting Five A/B Testing Statistical Myths

Busting Five A/B Testing Statistical Myths

In this episode of The Neural Nest, we pull back the curtain on some of the most common misunderstandings in A/B testing and experimentation. From p-values and sample sizes to stopping rules and statistical significance, we challenge five persistent myths that often distort decision-making in product development and data science. You’ll learn how to recognize flawed testing logic, why intuition can mislead even seasoned analysts, and how to design cleaner, more reliable experiments that actually drive impact. Whether you’re a data scientist, product manager, or just curious about how decisions get tested at scale, this episode will sharpen the way you think about evidence and experimentation.