According to a survey conducted in 2023, 74 percent of Americans in the United States said that they believed in God. In comparison, 87 percent of Americans who identified as Republicans and 66 percent of Americans who identified as Democrats shared this belief.

In 2022, around 31.6 percent of the global population were identify as Christian. Around 25.8 percent of the global population identify as Muslims, followed by 15.1 percent of global populations as Hindu.

In Brazil, 70 percent of the respondents believed in God as described in the holy scriptures, and another 19 percent believed in a higher power or spirit. In South Africa, the figures were 73 and 16 percent respectively. By contrast, less than one in five in Japan and only one in three in South Korea believed in God or some form of spirit or higher power.

Ukraine Population: Economically Inactive: Persons Who Believe That There is Number Work Available data was reported at 0.700 % in 2016. This stayed constant from the previous number of 0.700 % for 2015. Ukraine Population: Economically Inactive: Persons Who Believe That There is Number Work Available data is updated yearly, averaging 0.700 % from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.500 % in 2008. Ukraine Population: Economically Inactive: Persons Who Believe That There is Number Work Available data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.G004: Population: Economically Inactive.

Survey on Equipment and Use of Information and Communication Technologies in Households: Persons, by demographic characteristics, and individuals who believe that there are measures that encourage them in the use of the Internet. National.

Disability, Independence and Dependency Situations Survey: Population with disabilities that needs, but does not receive, personal care, according to who they believe should provide that care, by group of impairment and sex. National.

In a survey conducted in 2023 about the Australian publics' view on climate change, 12 percent stated that they did not believe that climate change was occurring. This was a lower proportion of respondents who shared this view compared to the beginning of the measured period, 2013.

Disability, Independence and Dependency Situations Survey: Population with disabilities that needs, but does not receive, personal care, according to who they believe should provide that care, by Autonomous Community and sex. Autonomous Community.

Disability, Independence and Dependency Situations Survey: Population with disabiltiies that seeks employment, according to the reason why they believe that they do not find work, by group of disability and sex. National.

Estimation of responses of organisms to their environment using experimental manipulations, and comparison of such responses across sets of species, is one of the primary tools in ecology research. The most common approach is to compare response of a single life stage of species to an environmental factor and use this information to draw conclusions about population dynamics of these species. Such approach ignores the fact that interspecific fitness differences measured at a single life stage are not directly comparable and cannot be extrapolated to lifetime fitness of individuals and thus species' population dynamics. Comparison of one life stage only while omitting demographic information can strongly bias conclusions, both in experimental studies with a few species, and in large comparative studies.

We illustrate the effect of this omission using both an exaggerated fictitious example, and biological data on congeneric species differing in their demography. We are showing, taking simple assumptions, that different demography can completely revert conclusions reached by a comparison based on an experiment focusing on a single life stage.

We show that a "demographic correction", namely translating observed effects into differences in outcomes of demographic models, is a solution to this problem. It requires turning the detected effects from the experiment into changes of transition probabilities of projection matrix models. Although such solution is limited by the low number of species with demographic data available, we believe that existing data (and data likely to be collected in the near future) permit at least approximate handling of this problem.

This table provides estimated data for the second quarter of 2024 on the population aged 18 and over who consider that tourists should pay a tourist tax in the Canary Islands according to the maximum amount, in euros, which they believe should be paid per person and day of stay. The information is disaggregated territorially at the level of large regions of the Canary Islands.

Background and objectivesHuman trafficking is a significant problem in which healthcare workers are in a unique position to intervene. This study sought to determine the self-reported knowledge levels of healthcare providers most likely to come in direct contact with victims of human trafficking.MethodsAn anonymous survey assessing self-reported knowledge of human trafficking was developed and distributed online. Demographic information and questions pertaining to training and knowledge of trafficking in a healthcare setting were asked. The primary outcomes were descriptive statistics and secondary outcomes were comparisons among demographic groups. Qualitative methodology via content analysis was implemented on an open-ended question.ResultsThe 6,603 respondents represented all regions of the country. Medical, nursing, and physician assistant students comprised 23% of the sample, while 40% were either physicians, fellows, or residents. Less than half the respondents (42%) have received formal training in human trafficking, while an overwhelming majority (93%) believe they would benefit by such training. Overall, respondents thought their level of knowledge of trafficking was average to below average (mean = 2.64 on a 5-point scale). There were significant differences in knowledge of trafficking by age group (p < .001), region (p < .001), and educational training level (p < .001). 949 respondents (14.4%) provided free-text comments that further described their opinions.ConclusionMost respondents stated they have not received training but felt they would benefit from it. There were significant differences between demographic groups. Further innovation is needed to design a universally appropriate curriculum on human trafficking that is accessible to all healthcare providers as well as mandatory training programs for healthcare institutions.

According to a November 2022 survey, just over one-third of the population in the United States believed they and their families would be better off in five years. Comparatively, 80 percent of Kenyans were optimistic about where they would be in five years' time.

Integrated population models (hereafter, IPMs) have become increasingly popular for the modeling of populations, as investigators seek to combine survey and demographic data to understand processes governing population dynamics. These models are particularly useful for identifying and exploring knowledge gaps within datasets, because they allow investigators to estimate biologically meaningful parameters, such as immigration and reproduction, that are uninformed by data. As IPMs have been developed relatively recently, model behavior remains relatively poorly understood. Much attention has been paid to parameter behavior such as parameter estimates near boundaries, as well as the consequences of dependent datasets. However, the movement of bias among parameters remains underexamined, particularly when models include parameters that are estimated without data. 2. To examine distribution of bias among model parameters, we simulated stable populations closed to immigration and emigration. We simulated two scenarios that might induce bias into survival estimates: marker induced bias in the capture-mark-recapture data, and heterogeneity in the mortality process. We subsequently ran appropriate capture-mark-recapture, state-space, and fecundity models, as well as integrated population models. 3. Simulation results suggest that when sampling bias exists in datasets, parameters that are not informed by data are extremely susceptible to bias. For example, in the presence of marker effects on survival of 0.1, estimates of immigration rate from an integrated population model were biased high (0.09). When heterogeneity in the mortality process was simulated, inducing bias in estimates of adult (-0.04) and juvenile (-0.097) survival rates, estimates of fecundity were biased by 46.2%. 4. We believe our results have important implications for biological inference when using integrated population models, as well as future model development and implementation. Specifically, parameters that are estimated without data absorb ~90% of the bias in integrated modelling frameworks. We suggest that investigators interpret posterior distributions of these parameters as a combination of biological process and systematic bias.

Demographic characteristics of Canada's military and veteran population: Canada, provinces and territories, census metropolitan areas and census agglomerations with partsFrequency: OccasionalTable: 98-10-0142-01Release date: 2023-11-15Geography: Canada, Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration partUniverse: Population aged 17 and over in private households, 2021 Census — 25% Sample dataVariable List: Visible minority (15), Religion (25), Generation status (4), Age (10B), Gender (3), Statistics (3), Military service status (4A)Footnotes: 1 Religion Religion refers to the person's self-identification as having a connection or affiliation with any religious denomination, group, body, or other religiously defined community or system of belief. Religion is not limited to formal membership in a religious organization or group. For infants or children, religion refers to the specific religious group or denomination in which they are being raised, if any. Persons without a religious connection or affiliation can self-identify as atheist, agnostic or humanist, or can provide another applicable response. 2 Generation status Generation status refers to whether or not the person or the person's parents were born in Canada. 3 Age 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date). 4 Gender Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender. 5 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. 6 Visible minority Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese." 7 Military service status Military service status refers to whether or not the person is currently serving or has previously served in the Canadian military. Military service status is asked of all Canadians aged 17 and older. For the purposes of the 2021 Census, Canadian military service includes service with the Regular Force or Primary Reserve Force as an Officer or Non-Commissioned Member. It does not include service with the Cadets, Cadet Organizations Administration and Training Service (COATS) instructors or the Canadian Rangers. 8 For more information on religion variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Religion Reference Guide, Census of Population, 2021. 9 For more information on generation status variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021. 10 Visible minority" refers to whether a person is a visible minority or not as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as "persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. In 2021 Census analytical and communications products the term "visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere."11 For more information on visible minority and population group variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2021. 12 For more information on the military service status variable, including data quality and comparability with other sources of data, please refer to the Canadian Military Experience Reference Guide, Census of Population, 2021.

Nigeria NG: Women Who Believe a Husband is Justified in Beating His Wife: When She Goes Out without Telling Him data was reported at 25.300 % in 2013. This records a decrease from the previous number of 32.200 % for 2008. Nigeria NG: Women Who Believe a Husband is Justified in Beating His Wife: When She Goes Out without Telling Him data is updated yearly, averaging 32.200 % from Dec 2003 (Median) to 2013, with 3 observations. The data reached an all-time high of 52.800 % in 2003 and a record low of 25.300 % in 2013. Nigeria NG: Women Who Believe a Husband is Justified in Beating His Wife: When She Goes Out without Telling Him data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Health Statistics. Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him.; ; Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys: STATcompiler (http://www.statcompiler.com/) as of November 22, 2016, UNICEF global databases (http://www.data.unicef.org/) as of November 2015. MICS Compiler (http://www.micscompiler.org/) as of June 12, 2016.; ;

Percent of coastline densely populated, by marine ecoregion.

The map shows the proportion of coastline (from the shore to within five kilometers of the coast) in each ecoregion where there are more than five hundred persons per square kilometer. By focusing attention on a narrow coastal strip, we believe that we are capturing areas with the highest likelihood of significant losses of intertidal and adjacent habitats as a result of building, dredging, land reclamation, and other forms of coastal engineering. It does not, of course, measure areas of coastal development per se and does not capture areas where aquaculture, agriculture, or low-density tourism have impacts.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3), Socioeconomic Data and Applications Center (SEDAC), Columbia University Palisades, New York. Available at http://sedac.ciesin.columbia.edu/gpw. Digital media.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

Reduction in population size and local extinctions have been reported for the yellow-bellied toad, Bombina variegata, but the genetic impact of this is not yet known. In this study, we genotyped 200 individuals, using mtDNA cytochrome b and 11 nuclear microsatellites. We investigated fine-scale population structure and tested for genetic signatures of historical and recent population decline, using several statistical approaches, including likelihood methods and approximate Bayesian computation. Five major genetically divergent groups were found, largely corresponding to geography but with a clear exception of high genetic isolation in a highly touristic area. The effective sizes in the last few generations, as estimated from the random association among markers, never exceeded a few dozen of individuals. Our most important result is that several analyses converge in suggesting that genetic variation was shaped in all groups by a 7- to 45-fold demographic decline, which occurred between a few hundred and a few 1000 years ago. Remarkably, only weak evidence supports recent genetic impact related to human activities. We believe that the alpine B. variegata populations should be monitored and protected to stop their recent decline and to prevent local extinctions, with highest priority given to genetically isolated populations. Nonetheless, current genetic variation pattern, being mostly shaped in earlier times, suggests that complete recovery can be achieved. In general, our study is an example of how the potential for recovery should be inferred even under the co-occurrence of population decline, low genetic variation, and genetic bottleneck signals.

Proportion of the population who believe their local police are doing a good job or an average or poor job at ensuring neighbourhood safety, by sex, population aged 15 and over.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations 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 interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2011-2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..When information is missing or inconsistent, the Census Bureau uses a method called imputation to assign values. Responses assigned using the Census Bureau's imputation method are called imputed values. The "Percent Imputed" section is the percent of respondents who received an imputed value for a particular subject. ..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates