The statistical genius and research of Dr. Snow not only saved lives, it anticipated the two main branches of modern statistics: descriptive statistics and inferential statistics.

Descriptive statistics organize, summarize, and communicate a group of numerical observations. Descriptive statistics describe large amounts of data in a single number or in just a few numbers. Here’s an illustration using familiar numbers: body weights. The Centers for Disease Control and Prevention (CDC, 2004, 2012) reported that people in the United States weigh more now than they did four decades ago. The average weight for women increased from 140.2 pounds in 1960 to 166.2 in 2010. For men, the average weight rose from 166.3 to 195.5 pounds in the same time span. These averages are descriptive statistics because they describe the weights of many people in just one number. A single number reporting the average communicates the observations more clearly than would a long list of weights for every person studied by the CDC.

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Inferential statistics use sample data to make general estimates about the larger population. Inferential statistics infer, or make an intelligent guess about, the population. For example, the CDC made inferences about weight even though it did not actually weigh everyone in the United States. Instead, the CDC studied a smaller representative group of U.S. citizens to make an intelligent guess about the entire population.

A sample is a set of observations drawn from the population of interest. Researchers usually study a sample, but they are really interested in the population, which includes all possible observations about which we’d like to know something. For example, the average weight of the CDC’s samples of women and men were used to estimate the average weight for the entire U.S. population, which was the CDC’s interest.

Samples are used most often because researchers are rarely able to study every person (or organization or laboratory rat) in a population. For one thing, it’s far too expensive. In addition, it would take too long. Snow did not want to interview every family in the Broad Street neighborhood—people were dying too fast! Fortunately, what he learned from his sample also applied to the larger population.