Subject categories, census geographies, and terminology are fairly consistent across most census datasets. This page provides a summary of the most common concepts. Use the Census Bureau's Glossary to find definitions for different terms.
The Census Bureau's public summary data is published for many different administrative, legal, and statistical areas. These areas fit into a hierarchy where larger areas are built from smaller ones, and smaller areas are constrained by larger ones. The diagram below illustrates hiw census geographies fit within summary levels. For example, counties are directly connected to states, which means that they fit within states and do not cross state boundaries. In contrast, places (cities and towns) do not have a connection to counties, which means they may cross county boundaries.
What about neighborhoods? Neighborhoods are areas that are locally and informally defined. The Census Bureau does not have a definition of what constitutes a neighborhood nor do they publish data for them. You would need to use a census geography to approximate the area of the neighborhood based on some consensus. A common approach is to aggregate census tracts into neighborhood-like areas. You can use either the PolicyMap or Social Explorer library databases to create customized areas (see the Library Databases on the Population Data page).
People and houses are summarized in a number of different categories and subcategories. Data is reported for each of these different groups, so for example there are distinct tables for household income versus family income. While these terms sound commonplace, in the census they are highly specific definitions. Basic tips appear below; see the glossary or technical documentation for individual datasets (decennial census or American Community Survey) for more details.
Variables published in the American Community Survey (ACS) are published with two values: an estimate and a margin of error (MOE). The estimate represents the mid-point of a range of possible values published at a 90% confidence level. The MOE indicates the range of values. For example, the estimate of people enrolled in college or university in the City of Providence was 25,912 +/- 1,100 between 2015-2019. This means this population could be as low as 24,812 or as high as 27,012. We are 90% confident that the estimate falls within this range; there is a 10% chance that the true value falls outside this range.
It's important to interpret ACS statistics as likely interval values and not as exact counts, and to scrutinize the MOE to gauge the precision and overall reliability of an estimate. A statistic called the coefficient of variation can be calculated to gauge this reliability. To reduce the size of the MOE, you can use 5-year estimates as opposed to 1-year estimates, or study a larger geographic area or population group. If you summarize or aggregate data, you must recalculate the MOE for the new values. To sum two estimates that represent totals, you add the estimates together, and then take the square root of the sum of squares of the MOEs for those estimates to create a new MOE. There are different formulas for calculating derived MOEs for proportions, ratios, and other estimates. See the resources below for additional info.