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'm an expert in data analysis and statistics with a deep understanding of various methodologies and data sources. My expertise lies in deciphering complex datasets and providing meaningful insights. I have hands-on experience in working with diverse statistical tools and have a keen eye for understanding the nuances and potential errors in data.
Now, let's dive into the concepts mentioned in the article you provided:
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Methodology Differences: The article mentions that methodology differences may exist between data sources, making estimates from different sources not directly comparable. This is a crucial point in data analysis, as variations in methodologies can impact the interpretation of results. Analysts must carefully consider these differences to avoid drawing inaccurate conclusions.
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Sampling Errors: The article notes that some estimates come from sample data, introducing sampling errors. Sampling errors can render apparent differences between geographies statistically indistinguishable. Analysts need to be aware of these errors and their potential impact on the reliability of the data.
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Vintage Years: The vintage year (e.g., V2023) refers to the final year of the series, and different vintage years of estimates are not comparable. Understanding vintage years is essential for tracking changes over time accurately. It's particularly mentioned that in Vintage 2022, Connecticut transitioned from eight counties to nine planning regions, indicating a significant shift that analysts should be aware of.
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ACS 5-year Estimates: Caution is advised when comparing 2018-2022 ACS 5-year estimates to other ACS estimates. The American Community Survey (ACS) is a critical data source, and understanding the guidelines for comparison is crucial for accurate interpretation.
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Race and Ethnicity Reporting: The article mentions that Hispanics may be of any race and are included in applicable race categories. This highlights the importance of considering the diversity within racial and ethnic categories and the need for inclusive reporting.
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Data Flags and Notations: Various flags and notations are used to indicate the reliability and limitations of the data. This includes flags like 'Value Flags,' 'F' for fewer than 25 firms, 'D' for suppressed data, 'N' for data not available, and others. Analysts must be familiar with these flags to make informed decisions based on the data.
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Data Sources for QuickFacts: The QuickFacts data are derived from a variety of sources, including Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, and more. Knowing the sources helps in understanding the breadth and depth of the data presented.
In summary, a thorough understanding of the nuances in methodology, sampling, vintage years, and specific data flags is crucial for accurate data analysis and interpretation in the context of the provided article.
