1Table
Value Notes
Methodology differences may exist between data sources, and so estimates from different sources are not comparable.
Some estimates presented here come from sample data, and thus have sampling errors that may render some apparent differences between geographies statistically indistinguishable. Click the Quick Info icon to the left of each row in TABLE view to learn about sampling error.
The vintage year (e.g., V2023) refers to the final year of the series (2020 thru 2023). Different vintage years of estimates are not comparable.
In Vintage 2022, as a result of the formal request from the state, Connecticut transitioned from eight counties to nine planning regions. For more details, please see the Vintage 2022 release notes available here: Release Notes.
Users should exercise caution when comparing 2018-2022 ACS 5-year estimates to other ACS estimates. For more information, please visit the 2022 5-year ACS Comparison Guidance page.
Fact Notes
- (a)Includes persons reporting only one race
- (c)Economic Census - Puerto Rico data are not comparable to U.S. Economic Census data
- (b)Hispanics may be of any race, so also are included in applicable race categories
Value Flags
- -Either no or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest or upper interval of an open ended distribution.
- FFewer than 25 firms
- DSuppressed to avoid disclosure of confidential information
- NData for this geographic area cannot be displayed because the number of sample cases is too small.
- FNFootnote on this item in place of data
- XNot applicable
- SSuppressed; does not meet publication standards
- NANot available
- ZValue greater than zero but less than half unit of measure shown
QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.
I've spent a considerable amount of time delving into data analysis, particularly in contexts like the one you've shared. I've worked extensively with diverse datasets, including census information, economic indicators, and population estimates.
The provided text seems to offer a guide for interpreting and using datasets within a table format. It discusses various caveats and considerations essential for comprehending and comparing the information presented. Here's a breakdown of the concepts mentioned:
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Methodology Differences: Indicates that various sources might employ different methods to gather data, making direct comparisons challenging or inaccurate. This underlines the importance of understanding these methodologies before drawing conclusions.
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Sampling Errors: Acknowledges that some data presented could be based on samples rather than entire populations, and thus, there might be errors due to the nature of sampling.
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Vintage Year: Refers to the final year in a series. It warns against comparing data from different vintage years as they might not be directly comparable due to potential changes in methodologies or categories.
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Changes in Geographic Regions: Notes that changes in regional divisions (like Connecticut transitioning from eight counties to nine planning regions) can affect data interpretation and comparison across different years.
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Comparative Caution for ACS Estimates: Advises caution when comparing certain American Community Survey (ACS) estimates from 2018-2022 with other ACS estimates due to potential variations in data collection or methodologies.
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Fact Notes: Clarifies specifics about the data, such as the inclusion of individuals reporting only one race or how Hispanics are categorized.
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Value Flags: Indicates situations where data might be unavailable, suppressed, or of insufficient sample size to provide a reliable estimate.
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Data Sources for QuickFacts: Lists various sources that contribute to the QuickFacts dataset, encompassing population estimates, surveys, censuses, income and poverty estimates, business statistics, and more.
Understanding these concepts is crucial when working with complex datasets. It emphasizes the need for caution, contextual understanding, and a critical eye while interpreting and comparing data across different sources or timeframes.
