Question: A virologist has 6 samples: 2 from virus strain A, 2 from strain B, and 2 from strain C. She randomly selects 4 samples to test. What is the probability that she selects exactly one sample from each strain?

Wed, 06 May 2026 19:18:07 +0000

**Uncovering the Hidden Probabilities in Virology Sample Selection** Why are mathematical models and risk assessment increasingly front-and-center in today’s life sciences conversation? With rapid advancements in virus research, understanding inheritance patterns, sampling distribution, and statistical likelihoods shapes not just science—but policy, public trust, and healthcare decisions. In this context, a straightforward question about sample selection provokes deeper discussion: A virologist has 6 samples—2 from strain A, 2 from strain B, and 2 from strain C. She randomly selects 4 to test. What is the probability she picks exactly one from each strain? This query, seemingly technical, reveals broader trends in data-driven research and informed decision-making within virology. This question isn’t just about probability—it reflects growing demand for precision in testing protocols, especially in laboratories handling multiple virus strains. With strains A, B, and C each represented twice, pinpoint selection matters. The goal: calculate the chance of drawing one sample from each strain when selecting four. This is a rich example of combinatorial probability with meaningful applications in risk analysis and experimental design. For curious readers interested in health science, genomics, or data literacy, understanding this problem builds intuition for statistical reasoning in real-world contexts. **Why This Query Resonates in US Scientific and Public Discussions**