Question: A philosopher of science examines 4 independent experiments, each with a 30% chance of confirming a hypothesis. What is the probability that exactly 1 experiment confirms the hypothesis?

Fri, 17 Apr 2026 09:04:42 +0000

**A Philosopher of Science and the Probability of One Success in Four Experiments** When breakthroughs seem elusive, it’s common to wonder how chance and logic interact—especially when evaluating scientific hypotheses under uncertainty. Consider this scenario: a philosopher of science analyzes four independent experiments, each with a 30% likelihood of confirming a long-standing hypothesis. What is the probability that exactly one of these tests yields a positive result? This question blends probability theory with real-world scientific practice, inviting both curiosity and critical thinking. Current trends in data literacy and skepticism toward guaranteed outcomes fuel interest in probability puzzles like this. In an era of rapid information exchange across US digital platforms, understanding the math behind experimental outcomes helps audiences assess uncertainty with greater clarity. The combination of four trials, each independently confirming the hypothesis 30% of the time, places us squarely in a classic binomial probability framework—relevant not just in philosophy but in clinical trials, AI validation, and risk modeling. From a statistical perspective, the probability of exactly one success among four independent trials—each with a 30% confirmation rate—is calculated using the binomial distribution formula. The core calculation reflects a fundamental concept: the chance of one specified event occurring amid multiple independent opportunities. This precise probability offers more than numbers; it provides clarity amid complexity.