Decisions versus Outcomes

Definition

A good decision and a good outcome are two different things, and confusing them is one of the most common ways people misjudge decision-making — their own and others'. Because decisions are made under uncertainty, a well-reasoned choice can still produce a bad result, and a poor choice can get lucky. As Stanford professor Ron Howard is quoted: "A good decision never turns bad, nor a bad decision good." The reason the distinction matters: "we control the decision; we do not control the outcome."

In the Book

Chapter 1 makes the case with a drug company that invested in a cancer treatment: the drug was approved, sales were huge, and executives congratulated themselves — until, eight years later, serious side effects surfaced, the drug was pulled, and lawsuits followed. Judged by outcome alone, the same decision would have to be called "first good and then bad," which the book calls impractical and often impossible to do fairly, since you'd have to wait until every consequence plays out. The book also sharpens the point with a texting-while-driving example (arriving home safely doesn't make texting and driving a good decision) and with R&D economics, where roughly 80% of projects are expected to fail — meaning good decision-making there requires accepting bad outcomes routinely, not eliminating them. Coauthor Carl Spetzler's own story closes the chapter: facing heart surgery with a 1-in-20 mortality estimate, he says the process of reaching a well-reasoned choice gave him peace of mind regardless of which way the outcome went — "it would still have been a good decision if I had been the one who stayed behind."

Why It Matters

This distinction blocks outcome bias (also called resulting) — the tendency to reverse-engineer a verdict on the process from whatever happened afterward. It lets you evaluate a choice honestly at the moment it's made, without waiting years for results, and it protects good decision-makers from being punished for bad luck while it stops lucky gamblers from being credited with skill. Any domain with meaningful uncertainty between choice and consequence — investing, medicine, hiring, poker, policy — needs this separation to actually learn from its own history instead of just rationalizing it.