Two worlds with remarkably similar challenges—and strikingly similar failures. Why Tech Should Pay Attention to Cancer Trials Most product teams believe A/B testing is a modern invention—lean, data-driven, and efficient. But if you step back, experimentation in tech today looks remarkably similar to oncology trials in the
A clinical trialist and a Bayesian statistician walk into a data monitoring committee meeting. The Bayesian wants to stop early based on posterior probabilities. The frequentist wants to reach the pre-specified sample size. Both are right. Both are wrong. Both want to adapt—just in radically different ways. Welcome to
In 2014, during a major snowstorm, Uber ran a randomized test by turning off surge pricing for a subset of riders. Some waited longer. Others never got a ride at all. It wasn’t just a pricing tweak. It was a trial of behavior, supply, and equity. Trials, once reserved
What happens when the ideal design for scientific clarity clashes with our instincts for patient autonomy? The Early Detection Initiative (EDI) for pancreatic cancer started with a bold design—then changed course. Here’s why it matters. Act I: The Trial That Got Ethics Committees Buzzing Imagine you’re reviewing
A simple trial. A massive effect. A p-value so small it didn't need confidence intervals. In 1988, ISIS-2 didn't just change cardiology—it proved that 162mg of aspirin could prevent 1 in 40 deaths. And it reminded statisticians what trial design is supposed to do: answer
