In our everyday lives, we are required to make decisions based upon our statistical intuitions. Often, these involve the comparison of two groups, such as luxury versus family cars and their suitability. Research has shown that the mean difference affects judgements where two sets of data are compared, but the variability of the data has only a minor influence, if any at all. However, prior research has tended to present raw data as simple lists of values. Here, we investigated whether displaying data visually, in the form of parallel dot plots, would lead viewers to incorporate variability information. In Experiment 1, we asked a large sample of people to compare two fictional groups (children who drank ‘Brain Juice’ versus water) in a one-shot design, where only a single comparison was made. Our results confirmed that only the mean difference between the groups predicted subsequent judgements of how much they differed, in line with previous work using lists of numbers. In Experiment 2, we asked each participant to make multiple comparisons, with both the mean difference and the pooled standard deviation varying across data sets they were shown. Here, we found that both sources of information were correctly incorporated when making responses. Taken together, we suggest that increasing the salience of variability information, through manipulating this factor across items seen, encourages viewers to consider this in their judgements. Such findings may have useful applications for best practices when teaching difficult concepts like sampling variation.

Comparison of exact expectations and standard deviations of times to coalescence Tk to their asymptotic approximations proposed by Chen and Chen (2013) [16], for n = 800.

Outliers correspond to fixes with location error (LE)>3 standard deviations from the mean location error of all fixes in the same habitat (i.e., without regard to the visibility category). The last two columns report on the mean number of outliers ± standard deviation across each visibility, and LERMS values calculated from all fixes in the same habitat after removal of outlier values.

GAD, Generalized Anxiety Disorder; MDD, Major Depressive Disorder; MADD, Mixed Anxiety-Depressive Disorder.

The mean and standard deviation, shown in brackets, of the behavioral measures and group comparison results.

These data are the standard error calculated from the AVISO Level 4 Absolute Dynamic Topography for Climate Model Comparison Number of Observations data set ( in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/aviso/L4/abs_dynamic_topo ). This data set is not meant to be used alone, but with the absolute dynamic topography data. These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison). The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid. These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - The first set of files represents projections of the number of historical (1901-1981) standard deviations (SD) above the historical mean for each of three future decades (2020-2029, 2050-2059, 2060-2069) temperature and precipitation levels.

The second set of files represents projections of the proportion of years in a future decade when monthly temperature or precipitation levels are at least two historical SDs above the historical mean.

Temperature and precipitation are monthly means and totals, respectively.

The spatial extent is clipped to a Seward REA boundary bounding box.

In the first set of files, each file, referred to as SDclasses, consists of ordered categorical (factor) data, with three unique classes (factor levels), coded 0, 1 and 2. Within each file, raster grid cells categorized as 0 represent those where the future decadal mean temperature or precipitation value does not exceed one historical SD above the historical mean. Cells categorized as 1 represent those where future decadal values exceed the historical mean by at least one but less than two historical SDs. Cells categorized as 2 represent those where future decadal values exceed the historical mean by at least two historical SDs.

In the second set of files, each file, referred to as annProp, consists of numerical data. Within each file, raster grid cell values are proportions, ranging from zero to one, representing the proportion of years in a future decade when monthly mean temperature or monthly total precipitation are at least two historical SD above the historical mean. Proportions are calculated on five GCMs and then averaged rather than calculated on the five-model composite directly.

Overview:

The historical monthly mean is calculated for each month as the 1901-1981 interannual mean, i.e., the mean of 82 annual monthly values.

The historical SD is calculated for each month as the 1901-1981 interannual SD, i.e., the SD of 82 annual monthly values.

2x2 km spatial resolution downscaled CRU 3.1 data is used as the historical baseline.

A five-model composite (average) of the Alaska top five AR4 2x2 km spatial resolution downscaled global circulation models (GCMs), using the A2 emissions scenario, is used for the future decadal datasets. This 5 Model Average is referred to by the acronym 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.edumethods.php for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[groupModel]_[timeFrame].[fileFormat]

[variable] pr, tas [metric] SDclasses, annProp [groupModel] 5modelAvg [timeFrame] decade_month [fileFormat] tif

examples:

pr_SDclasses_5modelAvg_2020s_01.tif

This file represents a spatially explicit map of the number of January total precipitation historical SDs above the January total precipitation historical mean level, for projected 2020-2029 decadal mean January total precipitation, where cell values are binned in classes less than one, at least one, less than two, and greater than two, labeled as 0, 1, and 2, respectively.

tas_annProp_5modelAVg_2060s_06.tif

This file represents a spatially explicit map of the proportion of years in the period 2060-2069 when June mean temperature projections are at least two historical SDs above the June mean temperature historical mean level, where cell values are proportions ranging from zero to one.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases

Implant stability quotient mean values, standard deviation and statistical comparison between the groups in each time of the evaluation.

*For simulations of approximately 1000 runs each for the two strain case for 40% vaccination rate. The mean of the number of ICU admissions on average using both the average and seasonal function vary a little bit across vaccination profiles due to runs (from particular combinations of parameters) resulting in a flag that made our pandemic filtering criteria exclude it.

Meta-analysis, which drives evidence-based practice, typically focuses on the average response of subjects to a treatment. For instance in nutritional research the difference in average weight of participants on different diets is typically used to draw conclusions about the relative efficacy of interventions. As a result of their focus on the mean, meta-analyses largely overlook the effects of treatments on inter-subject variability. Recent tools from the study of biological evolution, where inter-individual variability is one of the key ingredients for evolution by natural selection, now allow us to study inter-subject variability using established meta-analytic models. Here we use meta-analysis to study how low carbohydrate (LC) ad libitum diets and calorie restricted diets affect variance in mass. We find that LC ad libitum diets may have a more variable outcome than diets that prescribe a reduced calorie intake. Our results suggest that whilst LC diets are effective in a large proportion of the population, for a subset of individuals, calorie restricted diets may be more effective. There is evidence that LC ad libitum diets rely on appetite suppression to drive weight loss. Extending this hypothesis, we suggest that between-individual variability in protein appetite may drive the trends that we report. A priori identification of an individual's target intake for protein may help define the most effective dietary intervention to prescribe for weight loss.

Compared items’ means and standard deviations, and dimensions’ reliability estimates of Portuguese (Morais et al., 2014) and Spanish versions of the IBCP instrument

About this dataset

Context

The dataset presents median household incomes for various household sizes in United States, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in United States. Across the different household sizes in United States the mean income is $94,149, and the standard deviation is $26,829. The coefficient of variation (CV) is 28.50%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $38,463. It then further increased to $114,329 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/united-states-median-household-income-by-household-size.jpeg" alt="United States median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 United States median household income. You can refer the same here

View on Map View ArcGIS Service BTM Standard deviation – this mosaic dataset is part of a series of seafloor terrain datasets aimed at providing a consistent baseline to assist users in consistently characterizing Aotearoa New Zealand seafloor habitats. This series has been developed using the tools provided within the Benthic Terrain Model (BTM [v3.0]) across different multibeam echo-sounder datasets. The series includes derived outputs from 50 MBES survey sets conducted between 1999 and 2020 from throughout the New Zealand marine environment (where available) covering an area of approximately 52,000 km2. Consistency and compatibility of the benthic terrain datasets have been achieved by utilising a common projected coordinate system (WGS84 Web Mercator), resolution (10 m), and by using a standard classification dictionary (also utilised by previous BTM studies in NZ). However, we advise caution when comparing the classification between different survey areas.Derived BTM outputs include the Bathymetric Position Index (BPI); Surface Derivative; Rugosity; Depth Statistics; Terrain Classification. A standardised digital surface model, and derived hillshade and aspect datasets have also been made available. The index of the original MBES survey surface models used in this analysis can be accessed from https://data.linz.govt.nz/layer/95574-nz-bathymetric-surface-model-index/The full report and description of available output datasets are available at: https://www.doc.govt.nz/globalassets/documents/science-and-technical/drds367entire.pdf

comparison of the mean and standard error values in each condition for both the token and bar-pull paradigms

Comparison of best, worst, mean, median and standard deviation values for ABC, WOA, BOA PSO, EOSA, DE, GA, HGSO, SOA, and BMO metaheuristic algorithms using the classical benchmark functions over 500 runs and 100 population size.

Mean genetic standard deviation (Genetic SD) of parents in cycle 20 and loss in genetic standard deviation in cycle 20 in comparison to the genetic standard deviation in cycle 0 (Loss over cycle 0) for trait 1 (T1), trait 2 (T2) and the index trait using either optimal independent culling or index selection with different levels of accuracy, unfavourably correlated traits, and T2 relative economic importance of 1.0.

To assess geographic distributions of elements in the Arctic we compared essential and non-essential elements in the livers of polar bears (Ursus maritimus) collected from five regions within Canada in 2002, in Alaska between 1994 and 1999 and from the northwest and east coasts of Greenland between 1988 and 2000. As, Hg, Pb and Se varied with age, and Co and Zn with gender, which limited spatial comparisons across all populations to Cd, which was highest in Greenland bears. Collectively, geographic relationships appeared similar to past studies with little change in concentration over time in Canada and Greenland for most elements; Hg and Se were higher in some Canadian populations in 2002 as compared to 1982 and 1984. Concentrations of most elements in the polar bears did not exceed toxicity thresholds, although Cd and Hg exceeded levels correlated with the formation of hepatic lesions in laboratory animals.

This dataset has been archived and will no longer be updated as of 10/16/2024. For updated data, please refer to the ILINet State Activity Indicator Map.

Information on outpatient visits to health care providers for respiratory illness referred to as influenza-like illness (ILI) is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. More than 100 million patient visits were reported during the 2022-23 season. Each week, more than 3,000 outpatient health care providers around the country report to CDC the number of patient visits for ILI by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and ≥65 years) and the total number of visits for any reason. A subset of providers also reports total visits by age group. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat. Activity levels are based on the percent of outpatient visits due to ILI in a jurisdiction compared to the average percent of ILI visits that occur during weeks with little or no influenza virus circulation (non-influenza weeks) in that jurisdiction. The number of sites reporting each week is variable; therefore, baselines are adjusted each week based on which sites within each jurisdiction provide data. To perform this adjustment, provider level baseline ILI ratios are calculated for those that have a sufficient reporting history. Providers that do not have the required reporting history to calculate a provider-specific baseline are assigned the baseline ratio for their practice type. The jurisdiction level baseline is then calculated using a weighted sum of the baseline ratios for each contributing provider.

The activity levels compare the mean reported percent of visits due to ILI during the current week to the mean reported percent of visits due to ILI during non-influenza weeks. The 13 activity levels correspond to the number of standard deviations below, at, or above the mean for the current week compared with the mean during non-influenza weeks. Activity levels are classified as minimal (levels 1-3), low (levels 4-5), moderate (levels 6-7), high (levels 8-10), and very high (levels 11-13). An activity level of 1 corresponds to an ILI percentage below the mean, level 2 corresponds to an ILI percentage less than 1 standard deviation above the mean, level 3 corresponds to an ILI percentage more than 1 but less than 2 standard deviations above the mean, and so on, with an activity level of 10 corresponding to an ILI percentage 8 to 11 standard deviations above the mean. The very high levels correspond to an ILI percentage 12 to 15 standard deviations above the mean for level 11, 16 to 19 standard deviations above the mean for level 12, and 20 or more standard deviations above the mean for level 13.

Disclaimers:

The ILI Activity Indicator map reflects the intensity of ILI activity, not the extent of geographic spread of ILI, within a jurisdiction. Therefore, outbreaks occurring in a single area could cause the entire jurisdiction to display high or very high activity levels. In addition, data collected in ILINet may disproportionally represent certain populations within a jurisdiction, and therefore, may not accurately depict the full picture of respiratory illness activity for the entire jurisdiction. Differences in the data presented here by CDC and independently by some health departments likely represent differing levels of data completeness with data presented by the health department likely being more complete.

More information is available on FluView Interactive.

Comparison of mean and standard deviation for the manual versus automatic results of phagocytosis ratios.