This statistic depicts the results of a survey about the perception on online news and fake news in Italy in 2019. According to data, the largest group of users (37.6 percent) agreed that online news influenced the way people distinguished real news from fake news, whereas 34.7 percent completely believed that online news made difficult to tell what was a real fact from a fake new.

The statistic shows the share of adults who believe fake news is a major problem in the United States in 2017, sorted by age. During the survey, 80 percent of respondents aged 18 to 29 years stated that they believed fake news is a major problem.

The statistic shows the share of adults who believe fake news is a major problem in the United States as of March 2019, sorted by political affiliation. During the survey, 40 percent of Democrats or Democrat-leaning Independents stated that they believed fake news is a major problem in the United States, whereas 62 percent of Republicans said the same.

This dataset contains replication files for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples" by Raj Chetty and John Friedman. For more information, see https://opportunityinsights.org/paper/differential-privacy/. A summary of the related publication follows. Releasing statistics based on small samples – such as estimates of social mobility by Census tract, as in the Opportunity Atlas – is very valuable for policy but can potentially create privacy risks by unintentionally disclosing information about specific individuals. To mitigate such risks, we worked with researchers at the Harvard Privacy Tools Project and Census Bureau staff to develop practical methods of reducing the risks of privacy loss when releasing such data. This paper describes the methods that we developed, which can be applied to disclose any statistic of interest that is estimated using a sample with a small number of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic’s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.

Monthly data on federally administered Supplemental Security Income payments.

During a 2019 survey, 80 percent of respondents from Brazil stated that they believed that companies should stop advertising with any media platform that failed to prevent the spread of fake news and false information. The same was true for 74 percent of respondents from Mexico.

Statistics illustrates consumption, production, prices, and trade of Wigs, false beards, eyebrows and eyelashes, switches and the like and other articles n.e.s.; of human hair in Costa Rica from 2007 to 2024.

This statistical press release provides statistics for writs and originating summonses issued, cases disposed and orders made in respect of mortgages in the Chancery Division of the Northern Ireland High Court.

Source agency: Northern Ireland Statistics and Research Agency

Designation: National Statistics

Language: English

Alternative title: Mortgage Press Release

United States Diffusion Index: sa: Mfg: 3 Months Span data was reported at 67.100 Unit in Oct 2018. This records an increase from the previous number of 63.200 Unit for Sep 2018. United States Diffusion Index: sa: Mfg: 3 Months Span data is updated monthly, averaging 49.000 Unit from Jan 1991 (Median) to Oct 2018, with 334 observations. The data reached an all-time high of 82.200 Unit in Nov 1997 and a record low of 2.600 Unit in Mar 2009. United States Diffusion Index: sa: Mfg: 3 Months Span data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G041: Current Employment Statistics Survey: Diffusion Index.

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

Metropolitan Statistical Areas are CBSAs associated with at least one urbanized area that has a population of at least 50,000. The metropolitan statistical area comprises the central county or counties or equivalent entities containing the core, plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting.Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_nationgeo.gdb.zip Layer: Core_Based_Statistical_Area where [MEMI] = "1"Metadata: https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/series_tl_2023_cbsa.shp.iso.xml

United States Employment: NF: LH: Amusement Park & Arcade data was reported at 214.600 Person th in Oct 2018. This records a decrease from the previous number of 228.400 Person th for Sep 2018. United States Employment: NF: LH: Amusement Park & Arcade data is updated monthly, averaging 157.850 Person th from Jan 1990 (Median) to Oct 2018, with 346 observations. The data reached an all-time high of 265.900 Person th in Jul 2018 and a record low of 58.900 Person th in Jan 1990. United States Employment: NF: LH: Amusement Park & Arcade data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

About this dataset

Context

The dataset tabulates the population of Willamina by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Willamina across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.86% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Willamina is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Willamina total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Willamina Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Keytesville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Keytesville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.52% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Keytesville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Keytesville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Keytesville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Sibley by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Sibley across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.05% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Sibley is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Sibley total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sibley Population by Race & Ethnicity. You can refer the same here

United States Avg Weekly Earnings: OS: Dry Cleaning & Laundry ex Coin Operated data was reported at 528.540 USD in May 2018. This records a decrease from the previous number of 535.460 USD for Apr 2018. United States Avg Weekly Earnings: OS: Dry Cleaning & Laundry ex Coin Operated data is updated monthly, averaging 439.230 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 537.420 USD in Dec 2017 and a record low of 378.000 USD in Aug 2006. United States Avg Weekly Earnings: OS: Dry Cleaning & Laundry ex Coin Operated data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G032: Current Employment Statistics Survey: Average Weekly and Hourly Earnings.

About this dataset

Context

The dataset tabulates the population of Yetter by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Yetter across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.94% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Yetter is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Yetter total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Yetter Population by Race & Ethnicity. You can refer the same here

Explore the eCommerce statistics by industry and category for the year 2025. This page provides insights into the performance of different eCommerce categories, including store count, estimated sales amounts, products sold, and app spend. Gain a comprehensive understanding of the eCommerce landscape in 2025, with data-driven insights on market dynamics and consumer preferences. Stay informed about industry trends and benchmarks within specific eCommerce categories, empowering businesses to identify growth opportunities and optimize operations. This report is a valuable resource for industry professionals navigating the evolving world of eCommerce.

Statistics illustrates consumption, production, prices, and trade of Wigs, false beards, eyebrows and eyelashes, switches and the like and other articles n.e.s.; of human hair in Afghanistan from 2007 to 2024.