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.

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

aMixed between-within subjects analysis of variance – reported: interaction effect time x group (Wilk's Lambda).bCohens d calculated as the mean difference between groups divided by pooled standard deviation at baseline.*p

Standardized canonical discriminant function coefficient comparing variables in different scales (variables were adjusted by subtraction of its mean value and division by its standard deviation).

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.

Comparison of average nodal position errors (mean standard deviation values) derived from the filtering results for various types of measurement noise.

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

This table provides comparative data from 2004, 2009, 2015 and 2021 on the average, standard deviation, median and percentiles of the estimated weight in the population aged 16 and over in the Canary Islands by sex and age groups.

The mean, standard deviation and preservation of data (PD) of five data cleaning approaches with and without an algorithm (A) compared to uncleaned longitudinal growth measurements in Dogslife, SAVSNET and Banfield data.

The possibility to use colour data, a fast and inexpensive method of proxy data generation, extracted from two selected loess-paleosol sequences is discussed here. We compare the outcome from analysing outcrop images taking by digital cameras in the field and spectral colour data as determined under controlled laboratory conditions. By nature, differences can be expected due to differences in illumination, moisture, and sample preparation. Outcrop inclination may be an issue for photographs; correcting for this is possible when marks can be used for rectification. In both cases the data extracted from images match the visual impression of photos well, and are useful for obtaining a more quantitative measure for field observations. Smoothness (as measured by autocorrelation) is high for an image from Achenheim/France, where an image with a width of ca. 1.1 m and a depth of 1.6 m was analysed. Data from a narrower image part from Sanovita/Romania are noisier. In both example cases, a significant correlation between data extracted by digital image analysis and laboratory measurements could be established, suggesting that image analysis may be a useful tool where outcrop- and light-conditions allow useful photographs, especially where high resolution proxy data is required.

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

The repository includes:

1. The Auto_Throw code

2. The topographic data (folder “DEMs”). Note that Les Saintes bathymetric data are available at https://doi.org/10.17882/96053).

3. Analysis Auto_Throw outputs obtained in the study (folder “Analysis_outputs”); namely, the topographic profiles as analyzed by Auto_Throw (along with raw topographic profiles for comparison). Each file concerns a given fault, as indicated in the file name.

4. Result Auto_Throw outputs obtained in the study (folder “Results_outputs”); namely the interpreted results obtained for each fault and each calculation. Each file concerns a given fault, as indicated in the file name.

Each Fi_Synthesis file includes:

(a) A Table synthesizing the Fault, DEM, and Run parameters

(b) Results from far-field slope measures: we show maps of average far-field slope along each profile (far-field slopes are averaged over all calculations, and maps of far-field slope differences either side of the target scarp (averaged on each side over all calculations per profile;)

(c) The final measures obtained on the fault, for each run. Each page includes several figures: (A1): manual fault map (not used in the calculations); (A2): map of the final vertical offset measures. Profiles are shown (red), along with the polygon (black) within which measures are considered as concerning the target fault; (A3): 3D vision of the topographic profiles; (A4): Bivariate histogram showing the relative difference between any offset value and the final best offset along each profile (for a given scarp; i.e., within the polygon in A2), as a function of Th. Inset shows these differences globally; (B1): fault scarp and extracted topographic profiles. Fault tips are indicated in yellow; (B2): Final vertical offset profile, with measures undiscriminated or discriminated based on expert assessment. Uncertainties are standard deviation of offsets among the dense population of “most represented offsets”. If this population is small (e.g., Th < 40), the uncertainty is calculated as indicated in the corresponding pages (example in F5-Run 4); (B3): Final steepest (red) and mean (black) scarp dip profiles, with measures undiscriminated or discriminated based on expert assessment. Uncertainties on steepest dip are aleatory errors derived from the code, while uncertainties on mean dip are standard deviation of dip values among the dense population of “most represented offsets”; (B4): Final fault (black) and scarp (green) width profiles, with measures undiscriminated or discriminated based on expert assessment. Uncertainties on fault width are the standard deviation of the averaged values; (C1): Fault width measured at 2 lowest Th values; (C2): Fault width measured at 10 lowest Th values; (C3): Fault width measured at all Th values for population of “most represented offsets”.

When several runs have been done, or several faults combine into a larger-scale system, or our results need to be compared to prior measures, the compared or combined results are generally presented in the last few pages of the Fi_Synthesis file. For F5-Run 4, results are shown per individual fault. For Fish Slough, results are presented slightly differently as the objective is their comparison with those of Scott et al., 2022.

The monthly air temperature in 1153 stations and precipitation in 1202 stations in China and neighboring countries were collected to construct a monthly climate dataset in China on 0.025 ° resolution (approximately 2.5 km) named LZU0025 dataset designed by Lanzhou University (LZU), using a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of the LZU0025 was evaluated from analyzing three aspects: 1) Diagnostic statistics from surface fitting model in the period of 1951-2011, and results show low mean square root of generalized cross validation (RTGCV) for monthly air temperature surface (1.1 °C) and monthly precipitation surface (2 mm1/2) which interpolated the square root of itself. This indicate exact surface fitting models. 2) Error statistics based on 265 withheld stations data in the period of 1951-2011, and results show that predicted values closely tracked true values with mean absolute error (MAE) of 0.6 °C and 4 mm and standard deviation of mean error (STD) of 1.3 °C and 5 mm, and monthly STDs presented consistent change with RTGCV varying. 3) Comparisons to other datasets through two ways, one was to compare three indices namely the standard deviation, mean and time trend derived from all datasets to referenced dataset released by the China Meteorological Administration (CMA) in the Taylor diagrams, the other was to compare LZU0025 to the Camp Tibet dataset on mountainous remote area. Taylor diagrams displayed the standard deviation derived from LZU had higher correlation with that induced from CMA (Pearson correlation R=0.76 for air temperature case and R=0.96 for precipitation case). The standard deviation for this index derived from LZU was more close to that induced from CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. The same superior performance of LZU were found in comparing indices of the mean and time trend derived from LZU and those induced from other datasets. LZU0025 had high correlation with the Camp dataset for air temperature despite of insignificant correlation for precipitation in few stations. Based on above comprehensive analyses, LZU0025 was concluded as the reliable dataset.

This table provides comparative data from 2009, 2015 and 2021 on the mean and estimated standard deviation of health-related quality of life in the population aged 8 to 15 years. The information is disaggregated territorially at the level of large regions of the Canary Islands.

Mg/Ca-based SST and thermocline temperatures with 1Sigma error at different sites, their difference (DeltaT), and the average DeltaT values for different periods with 95% confidence interval (CI). Also shown are hydrogen isotope (dD) values of n-C31 alkanes relative to the standard mean ocean water (per mil SMOW) with the 95% CI (LGM values are corrected for ice-volume), the C31 n-alkane concentrations in nanogram per gram sediment (ng g-1), the Carbon Preference Index (CPI27-33) as a measure of the degree of terrestrial organic matter degradation (higher CPI reflects less degraded plant waxes and vice versa), and their 1Sigma standard deviations (SD).

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.

This table provides comparative data from 2009, 2015 and 2021 on the mean and estimated standard deviation of mental health scales in the population aged 4 to 15 years in the Canary Islands by sex and age groups.

Mean, within-run standard deviation (SDw-run) and within-run coefficient of variation (CVw-run) in percent from one blood sample analyzed for each of the 3 analyzers (cobas b 123, VetStat and epoc) and compared to published precision targets [21].

This table provides comparative data from 2015 and 2021 on the mean and estimated standard deviation of the age of onset of smoking in the population aged 16 years and older smoker and ex-smoker. The information is disaggregated territorially at the level of large regions of the Canary Islands.

The monthly air temperature in 1153 stations and precipitation in 1202 stations in China and neighboring countries were collected to construct a monthly climate dataset in China on 0.025 ° resolution (approximately 2.5 km) named LZU0025 dataset designed by Lanzhou University (LZU), using a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of the LZU0025 was evaluated from analyzing three aspects: 1) Diagnostic statistics from surface fitting model in the period of 1951-2011, and results show low mean square root of generalized cross validation (RTGCV) for monthly air temperature surface (1.1 °C) and monthly precipitation surface (2 mm1/2) which interpolated the square root of itself. This indicate exact surface fitting models. 2) Error statistics based on 265 withheld stations data in the period of 1951-2011, and results show that predicted values closely tracked true values with mean absolute error (MAE) of 0.6 °C and 4 mm and standard deviation of mean error (STD) of 1.3 °C and 5 mm, and monthly STDs presented consistent change with RTGCV varying. 3) Comparisons to other datasets through two ways, one was to compare three indices namely the standard deviation, mean and time trend derived from all datasets to referenced dataset released by the China Meteorological Administration (CMA) in the Taylor diagrams, the other was to compare LZU0025 to the Camp Tibet dataset on mountainous remote area. Taylor diagrams displayed the standard deviation derived from LZU had higher correlation with that induced from CMA (Pearson correlation R=0.76 for air temperature case and R=0.96 for precipitation case). The standard deviation for this index derived from LZU was more close to that induced from CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. The same superior performance of LZU were found in comparing indices of the mean and time trend derived from LZU and those induced from other datasets. LZU0025 had high correlation with the Camp dataset for air temperature despite of insignificant correlation for precipitation in few stations. Based on above comprehensive analyses, LZU0025 was concluded as the reliable dataset. Supplement to: Zhao, Hong; Huang, Wei; Xie, Tingting; Wu, Xian; Xie, Yaowei; Feng, Song; Chen, Fahu (2019): Optimization and evaluation of a monthly air temperature and precipitation gridded dataset with a 0.025° spatial resolution in China during 1951-2011. Theoretical and Applied Climatology, 1-17