Population health analysis relies on looking at groups, not just individuals. By aggregating data across people within a population or subgroups, we create summary measures—rates, averages, proportions—that reveal trends over time or differences between places. This is how we spot rising or falling health outcomes, geographic patterns, and disparities, and then use that insight to guide interventions and resources. The idea that data are never aggregated or that aggregated data are only for marketing doesn’t fit how population health works, and saying data aren’t used in population health is simply incorrect.

Genetic variation among individuals in a population refers to the differences in their genetic makeup, specifically the variety of alleles that exist within that group. This diversity is the term used to describe the variation present, and it serves as the raw material that evolution acts upon. Mutation would be a source of new variation, not the variation itself. Adaptation is the process by which traits that improve survival become more common over time. Genotype describes the specific genetic constitution of an individual, not the variation across the population. So the term that best fits the prompt is genetic variation.

Social and community context centers on the social environment—the relationships and networks that provide support and resources. Social support fits this best because it embodies emotional, informational, and practical help from family, friends, or community members, which directly influences health and well-being. Transportation and walkability describe the built environment and infrastructure, not the social ties that people rely on. Safety concerns protection from harm and can be shaped by both social and physical factors, but it doesn’t inherently reflect the social networks and supports at the heart of this context.

Curiosity starts the process by turning an observation into a focused, testable question. This step defines what you’re trying to learn and guides how you’ll design experiments, what data you’ll collect, and how you’ll evaluate results. Without a clear question, the rest of the work—forming a hypothesis, designing tests, gathering data, and analyzing outcomes—has no direction. So the first move is to pose a question or identify a problem based on what you’ve noticed. The next steps build on that question: you’d propose a hypothesis, design and run experiments to test it, collect data, and analyze what the results mean. But all of those follow from having a specific question to answer in the first place.