A herpetologist studying a snake population in Java finds that 30% of the snakes carry a specific parasite. If she randomly samples 10 snakes, what is the probability that exactly 3 carry the parasite, using the binomial distribution?

Tue, 05 May 2026 01:54:35 +0000

**Why a 30% Parasite Rate in Java’s Snakes Matters Now—And How Math Explains the odds** Wildlife health studies are painting a clearer picture of how parasites shape ecosystems, and one recent study in Java has sparked quiet but meaningful interest. As tropical biodiversity turns more urgent under climate and land-use pressures, tracking pathogens in wild populations offers critical insight—especially for conservation planning. For audiences following science, ecology, or public health trends in Southeast Asia, the statistic that 30% of a snake population carry a specific parasite isn’t just numbers—it’s a signal of vulnerability and adaptation in a dynamic environment. This statistic points to a critical window: understanding transmission patterns in wild populations helps scientists predict outbreaks, protect species, and inform sustainable land use. But for researchers analyzing such data, a fundamental question emerges: What’s the likelihood of observing exactly 3 infected snakes in a random sample of 10? This query bridges intuition and probability—key to accurate scientific reporting and informed decision-making. Using the binomial distribution offers a reliable way to calculate this. It applies when each “trial” (here, examining one snake) has only two outcomes and a constant success probability. In this case, each snake is assessed as “carrying the parasite” (success) with 30% probability, or “not carrying” (failure) with 70%. With 10 snakes sampled, the binomial model estimates how often exactly 3 are infected, shaping how we interpret rare but meaningful health trends.