Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what's behind the doors, opens another door, say No. 3, which has a goat. He then says to you, ``Do you want to pick door No. 2?'' Is it to your advantage to switch your choice? The answer is ``yes'' but the literature offers many reasons why this is the correct answer. The present paper argues that the most common reasoning found in introductory statistics texts, depending on making a number of ``obvious'' or ``natural'' assumptions and then computing a conditional probability, is a classical example of solution driven science. The best reason to switch is to be found in von Neumann's minimax theorem from game theory, rather than in Bayes' theorem.

翻译：假设你参加一个游戏节目，面前有三扇门：一扇门后有一辆汽车，另外两扇门后面是山羊。你选择了一扇门，比如1号门，知道门后情况的主持人打开了另一扇门，比如3号门，后面是一只山羊。然后他问你：“你想换成2号门吗？”换门是否对你有利？答案是“是的”，但文献中提供了许多理由来解释为什么这是正确答案。本文认为，在入门统计学教材中最常见的推理——即基于一系列“显而易见的”或“自然的”假设，然后计算条件概率——是“解题驱动科学”的典型例子。换门的最佳理由应来自冯·诺伊曼的博弈论中的极小化极大定理，而非贝叶斯定理。