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Reader’s Guide

The Condition of Education contains indicators on the state of education in the United States, from prekindergarten through postsecondary education, as well as labor force outcomes and international comparisons. Each indicator uses both text and figures to present a narrative that highlights key findings and significant patterns in the data. It is important to note that all data from the figures may not be described in the text and all data in the text may not be presented in the figures. The data source(s) for the indicator can be found in the Reference tables section at the end of the indicator. For more information about data sources, see the Data Sources and Estimates section below.

Readers can browse the full report online through the HTML site or download PDFs of the full report or individual indicators. In both the PDF and HTML versions, indicators are hyperlinked to tables in the Digest of Education Statistics. These tables contain the source data used in the most recent edition of The Condition of Education.

Data Sources and Estimates

The data in these indicators were obtained from many different sources—including students and teachers, state education agencies, elementary and secondary schools, and colleges and universities—using surveys and compilations of administrative records. Users should be cautious when comparing data from different sources. Differences in aspects such as procedures, timing, question phrasing, and interviewer training can affect the comparability of results across data sources.

Most indicators in The Condition of Education summarize data from surveys conducted by the National Center for Education Statistics (NCES) or by the U.S. Census Bureau with support from NCES. Brief descriptions of the major NCES surveys used in these indicators can be found in the Guide to Sources. More detailed descriptions can be obtained on the NCES website under “Surveys and Programs.”

The Guide to Sources also includes information on non-NCES sources used to develop indicators, such as the Census Bureau’s American Community Survey (ACS) and Current Population Survey (CPS). For details on the ACS, see For details on the CPS, see

Data for The Condition of Education indicators are obtained from two types of surveys: universe surveys and sample surveys. In universe surveys, information is collected from every member of the population. For example, in a survey regarding expenditures of public elementary and secondary schools, data would be obtained from each school district in the United States. When data from an entire population are available, estimates of the total population or a subpopulation are made by simply summing the units in the population or subpopulation. As a result, there is no sampling error, and observed differences are reported as true.

Since universe surveys are often expensive and time consuming, many surveys collect data from a sample of the population of interest (sample surveys). For example, the National Assessment of Educational Progress (NAEP) assesses a representative sample of students rather than the entire population of students. When a sample survey is used, statistical uncertainty is introduced because the data come from only a portion of the entire population. This statistical uncertainty must be considered when reporting estimates and making comparisons. For more information, please see the section on standard errors below.

Various types of statistics derived from universe and sample surveys are reported in The Condition of Education. Many indicators report the size of a population or subpopulation, and the size of a subpopulation is often expressed as a percentage of the total population. In addition, the average (or mean) value of some characteristic of the population or subpopulation may be reported. The average is obtained by summing the values for all members of the population and dividing the sum by the size of the population. An example is the annual average salaries of full-time instructional faculty at degree-granting postsecondary institutions. Another measure that is sometimes used is the median. The median is the midpoint value of a characteristic at or above which 50 percent of the population is estimated to fall and at or below which 50 percent of the population is estimated to fall. An example is the median annual earnings of young adults who are full-time, full-year wage and salary workers.

Standard Errors

Using estimates calculated from data based on a sample of the population requires consideration of several factors before the estimates become meaningful. When using data from a sample, some margin of error will always be present in estimations of characteristics of the total population or subpopulation because the data are available from only a portion of the total population. Consequently, data from samples can provide only an approximation of the true or actual value. The margin of error of an estimate—i.e., the range of potential true or actual values—depends on several factors, such as the amount of variation in the responses, the size and representativeness of the sample, and the size of the subgroup for which the estimate is computed. The magnitude of this margin of error is measured by what statisticians call the standard error of an estimate. A larger standard error typically indicates that the estimate is less precise, while a smaller standard error typically indicates that the estimate is more precise.

When data from sample surveys are reported, the standard error is calculated for each estimate. The standard errors for all estimated totals, means, medians, or percentages are reported in the reference tables.

In order to caution the reader when interpreting findings in the indicators, estimates from sample surveys are flagged with a “!” when the standard error is between 30 and 50 percent of the estimate, and estimates are suppressed and replaced with a “‡” when the standard error is 50 percent of the estimate or greater.

Data Analysis and Interpretation

When estimates are from a sample, caution is warranted when drawing conclusions about whether one estimate is different in comparison to another; whether a time series of estimates is increasing, decreasing, or staying the same; or whether two variables are associated. Although one estimate may appear to be larger than another, a statistical test may find that the apparent difference between them is not measurable due to the uncertainty around the estimates. In this case, the estimates are described as having no measurable difference, meaning the difference between them is not statistically significant.

Whether differences in means or percentages are statistically significant can be determined using the standard errors of the estimates. In the indicators in The Condition of Education and other NCES reports, when differences are statistically significant, the probability that the difference occurred by chance is less than 5 percent, according to NCES standards.

For all indicators that report estimates based on samples, differences between estimates (including increases and decreases) are stated only when they are statistically significant. To determine whether differences are statistically significant, most indicators use two-tailed t tests at the .05 level. The t test formula for determining statistical significance is adjusted when the samples being compared are dependent. The analyses are not adjusted for multiple comparisons, with the exception of indicators that use NAEP data. All analyses in NAEP indicators are conducted using the NAEP Data Explorer, which makes adjustments for comparisons involving a variable with more than two categories. The NAEP Data Explorer makes such adjustments using the Benjamini-Hochberg False Discovery Rate. When the variables to be tested are postulated to form a trend over time, the relationship may be tested using linear regression or ANOVA trend analyses instead of a series of t tests. Indicators that use other methods of statistical comparison include a separate technical notes section. For more information on data analysis, see the NCES Statistical Standards, Standard 5-1.

Multivariate analyses, such as ordinary least squares (OLS) regression models, provide information on whether the relationship between an independent variable and an outcome measure (such as group differences in the outcome measure) persists after taking into account other variables (such as student, family, and school characteristics). For indicators that include a regression analysis, multiple categorical or continuous independent variables are entered simultaneously. A significant regression coefficient indicates an association between the dependent (outcome) variable and the independent variable, after controlling for other independent variables included in the regression analysis.

Data presented in the indicators typically do not investigate more complex hypotheses or support causal inferences. We encourage readers who are interested in more complex questions and in-depth analyses to explore other NCES resources, including publications, online data tools, and public- and restricted-use datasets at

A number of considerations influence the ultimate selection of the data years to feature in the indicators. To make analyses as timely as possible, the latest year of available data is shown. The choice of comparison years is often based on the need to show the earliest available survey year, as in the case of the NAEP and the international assessment surveys. In the case of surveys with long time frames, such as surveys measuring enrollment, a decade’s beginning year (e.g., 1990 or 2000) often starts the trend line. In the figures and tables of the indicators, intervening years are selected in increments in order to show the general trend. The narrative for the indicators typically compares the most current year’s data with those from the initial year and then with those from a more recent year. Where applicable, the narrative may also note years in which the data begin to diverge from previous trends.

Rounding and Other Considerations

All calculations within the indicators in this report are based on unrounded estimates. Therefore, the reader may find that a calculation cited in the text or figure, such as a difference or a percentage change, may not be identical to the calculation obtained by using the rounded values shown in the accompanying tables. Although values reported in the reference tables are generally rounded to one decimal place (e.g., 76.5 percent), values reported in each indicator are generally rounded to whole numbers (with any value of 0.50 or above rounded to the next highest whole number). Due to rounding, cumulative percentages may sometimes equal 99 or 101 percent rather than 100 percent. While the data labels on the figures have been rounded to whole numbers, the graphical presentation of these data is based on the unrounded estimates.

Race and Ethnicity

The Office of Management and Budget (OMB) is responsible for the standards that govern the categories used to collect and present federal data on race and ethnicity. The OMB revised the guidelines on racial/ethnic categories used by the federal government in October 1997, with a January 2003 deadline for implementation. The revised standards require a minimum of these five categories for data on race: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, and White. The standards also require the collection of data on ethnicity categories: at a minimum, Hispanic or Latino and Not Hispanic or Latino. It is important to note that Hispanic origin is an ethnicity rather than a race, and, therefore, persons of Hispanic origin may be of any race. Origin can be viewed as the heritage, nationality group, lineage, or country of birth of the person or the person’s parents or ancestors before their arrival in the United States. The race categories White, Black, Asian, Native Hawaiian or Other Pacific Islander, and American Indian or Alaska Native, as presented in these indicators, exclude persons of Hispanic origin unless noted otherwise.

The categories are defined as follows:

American Indian or Alaska Native: A person having origins in any of the original peoples of North and South America (including Central America) and maintaining tribal affiliation or community attachment.

Asian: A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent, including Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.

Black or African American: A person having origins in any of the black racial groups of Africa.

Native Hawaiian or Other Pacific Islander: A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.

White: A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.

Hispanic or Latino: A person of Mexican, Puerto Rican, Cuban, South or Central American, or other Spanish culture or origin, regardless of race.

Within these indicators, some of the category labels have been shortened in the text, tables, and figures for ease of reference. American Indian or Alaska Native is denoted as American Indian/Alaska Native (except when separate estimates are available for American Indians alone or Alaska Natives alone); Black or African American is shortened to Black; Native Hawaiian or Other Pacific Islander is shortened to Pacific Islander; and Hispanic or Latino is shortened to Hispanic.  

The indicators in this report draw from a number of different data sources. Many are federal surveys that collect data using the OMB standards for racial/ethnic classification described above; however, some sources have not fully adopted the standards, and some indicators include data collected prior to the adoption of the standards. This report focuses on the six categories that are the most common among the various data sources used: White, Black, Hispanic, Asian, Pacific Islander, and American Indian/Alaska Native. Asians and Pacific Islanders are combined into one category in indicators for which the data were not collected separately for the two groups.

Some of the surveys from which data are presented in these indicators give respondents the option of selecting either an “other” race category, a “Two or more races” or “multiracial” category, or both. Where possible, indicators present data on the “Two or more races” category; in some cases, however, this category may not be separately shown because the information was not collected or because of other data issues. In general, the “other” category is not separately shown. Any comparisons made between persons of one racial/ethnic group to “all other racial/ethnic groups” include only the racial/ethnic groups shown in the indicator. In some surveys, respondents are not given the option to select more than one race. In these surveys, respondents of Two or more races must select a single race category. Any comparisons between data from surveys that offer the option to select more than one race and surveys that do not offer such an option should take into account the fact that there is a potential for bias if members of one racial group are more likely than members of other racial groups to identify themselves as “Two or more races.”1 For postsecondary data, foreign students are counted separately and are therefore not included in any racial/ethnic category.

More detailed information on racial/ethnic groups, including data for specific Asian and Hispanic ancestry subgroups (such as Mexican, Puerto Rican, Chinese, or Vietnamese) can be found in the Status and Trends in the Education of Racial and Ethnic Groups report.

Limitations of the Data

The relatively small sizes of the American Indian/Alaska Native and Pacific Islander populations pose many measurement difficulties when conducting statistical analyses. Even in larger surveys, the numbers of American Indians/Alaska Natives and Pacific Islanders included in a sample are often small. Researchers studying data on these two populations often face small sample sizes that reduce the reliability of results. Survey data for American Indians/Alaska Natives and Pacific Islanders often have somewhat higher standard errors than data for other racial/ethnic groups. Due to large standard errors, differences that seem substantial are often not statistically significant and, therefore, are not cited in the text.

Data on American Indians/Alaska Natives are often subject to inconsistencies in how respondents identify their race/ethnicity. According to research on the collection of race/ethnicity data conducted by the Bureau of Labor Statistics in 1995, the categorization of American Indian and Alaska Native is the least stable self-identification. The racial/ethnic categories presented to a respondent, and the way in which the question is asked, can influence the response, especially for individuals who consider themselves as being of mixed race or ethnicity.

As mentioned above, Asians and Pacific Islanders are combined into one category in indicators for which the data were not collected separately for the two groups. The combined category can sometimes mask significant differences between subgroups. For example, prior to 2011, NAEP collected data that did not allow for separate reporting of estimates for Asians and Pacific Islanders. Information from the Digest of Education Statistics 2019 (table 101.20), based on the Census Bureau’s Current Population Reports, indicates that 96 percent of all Asian/Pacific Islander 5- to 24-year-olds are Asian. This combined category for Asians/Pacific Islanders is more representative of Asians than Pacific Islanders.


In accordance with the NCES Statistical Standards, many tables in this volume use special symbols to alert the reader to various statistical notes. These symbols and their meanings are as follows:

— Not available.
† Not applicable.
# Rounds to zero.
! Interpret data with caution. The coefficient of variation (CV) for this estimate is between 30 and 50 percent.
‡ Reporting standards not met. Either there are too few cases for a reliable estimate or the coefficient of variation (CV) for this estimate is 50 percent or greater.
* p < .05 significance level.

1 See Parker, J.D., Schenker, N., Ingram, D.D., Weed, J.A., Heck, K.E., and Madans, J.H. (2004). Bridging Between Two Standards for Collecting Information on Race and Ethnicity: An Application to Census 2000 and Vital Rates. Public Health Reports, 119(2): 192–205. Retrieved April 25, 2017, from