Context MIQEs: One way to assess people's distribution perception is MIQEs: Mean Interquantile Estimations. MIQEs are assessed by asking: if we order the values of a distribution according to their size and then form equal-sized groups from the lowest values to the highest values, what is the mean value of each group? (For example, what are the mean values of four groups, each containing 25% of values?) Prior findings: In prior studies, we showed participants profiles of fictitious employees including their monthly income and then assessed MIQEs in regards to this income (e.g. “What is the mean income of the highest earning 25% of employees?”). In these experiments subject’s estimations are reliably far more linear than appropriate especially for distributions with negative skew. Bias explanations: We hypothesize that this linearity bias is due to uncertainty regarding the distribution. We furthermore expect this uncertainty to depend (among other factors) on the congruency of subjects’ expectations towards the distribution. In other words, when people see a distribution that does not fit their expectation, they notice incongruency. This incongruency leads to uncertainty about the distribution and the need to fall back on a simple bias. This bias is to adhere to linearity when producing estimations about the distribution. Goal

The current experiment will test if participants' uncertainty about the distribution mediates between congruency of expectation and linearity of estimations.

Method The experiment consists of two blocks: an expectation block and an estimation block. In the expectations block subjects are told that they will see 8 numbers of a distribution to learn about the task they will have to perform in the estimation block, i.e. producing MIQEs. They will then be presented with a task description and afterwards see a trial run consisting of 8 numbers following either an equal distribution, positively skewed distribution or negatively skewed distribution. All numbers will be presented without any contextual setting (without physical or monetary unit) to avoid preexisting expectations. After the presentation of these 8 numbers they will be asked to estimate the mean value of the smallest 2 numbers (MIQE_1), the second-smallest (MIQE_2), the second-largest (MIQE_3), and the largest (MIQE_4). Afterwards they will be told the correct MIQEs for these 8 numbers and that these 8 numbers are part of a larger distribution that they are about to see in the estimation block.

In the estimation block subjects will be presented with either a positively or a negatively skewed distribution. After the presentation of 40 numbers, they will be asked to assess their uncertainty in regard to the distribution: How certain do you feel about your memory of the 40 numbers you have just seen? Then they will be asked to produce MIQEs for the 40 numbers.

Manipulation Expectation skew: The 8 numbers presented in the expectation block have negative skew, zero skew or positive skew. Distribution skew: The 40 numbers presented in the estimation block have negative skew or positive skew.

Hypothesis

We hypothesize that we will find the following results:

1. Subject's MIQEs reflect the skew of the presented distribution (positive skew for the positively distribution, negative skew for the negatively skewed distribution. This will show in a main effect Distribution Skew on MIQE Skew.

2. Subject’s MIQEs are more linear the more incongruent their expectations are. The skew of their MIQEs is closer to zero when incongruency is high. This will show in an interaction effect of the factors Congruency and Distribution Skew on MIQE Skew.

3. Subject’s MIQEs are less accurate the more incongruent their expectations are. The Root Mean Square Error of the MIQEs increases with increasing incongruency of expectation and distribution. This will show in a main effect congruency on the Root Mean Square Error of MIQEs.

4. The linearity of subjects’ MIQEs is mediated by their reported uncertainty about the distribution. This will show in an indirect effect of Congruency on MIQE Skew.

Reproducibility package for the article:Reaction times and other skewed distributions: problems with the mean and the medianGuillaume A. Rousselet & Rand R. Wilcoxpreprint: https://psyarxiv.com/3y54rdoi: 10.31234/osf.io/3y54rThis package contains all the code and data to reproduce the figures and analyses in the article.

This dataset contains upper air Skew-T Log-P data collected at Denver during the HIPPO-4 project. The imagery are in GIF format. The imagery cover the time span from 2011-06-08 12:00:00 to 2011-07-13 12:00:00.

Data are aggregated over all for each country.

A dataset of mentions, growth rate, and total volume of the keyphrase 'Skewed Distribution' over time.

Quantitative-genetic models of differentiation under migration-selection balance often rely on the assumption of normally distributed genotypic and phenotypic values. When a population is subdivided into demes with selection toward different local optima, migration between demes may result in asymmetric, or skewed, local distributions. Using a simplified two-habitat model, we derive formulas without a priori assuming a Gaussian distribution of genotypic values, and we find expressions that naturally incorporate higher moments, such as skew. These formulas yield predictions of the expected divergence under migration-selection balance that are more accurate than models assuming Gaussian distributions, which illustrates the importance of incorporating these higher moments to assess the response to selection in heterogeneous environments. We further show with simulations that traits with loci of large effect display the largest skew in their distribution at migration-selection balance.

1. The performance of ectotherms integrated over time depends in part on the position and shape of the distribution of body temperatures (Tb) experienced during activity. For several complementary reasons, physiological ecologists have long expected that Tb distributions during activity should have a long left tail (left-skewed); but only infrequently have they quantified the magnitude and direction of Tb skewness in nature.
2. To evaluate whether left-skewed Tb distributions are general for diurnal desert lizards, we compiled and analyzed Tb (∑ = 9,023 temperatures) from our own prior studies of active desert lizards on three continents (25 species in Western Australia, 10 in the Kalahari Desert of Africa, and 10 species in western North America). We gathered these data over several decades, using standardized techniques.
3. Many species showed significantly left-skewed Tb distributions, even when records were restricted to summer months. However, magnitudes of skewness were always small, such that mean Tb were never more than 1°C lower than median Tb. The significance of Tb skewness was sensitive to sample size, and power tests reinforced this sensitivity.
4. The magnitude of skewness was not obviously related to phylogeny, desert, body size, or median body temperature. Moreover, formal phylogenetic analysis is inappropriate because geography and phylogeny are confounded (that is, are highly collinear).
5. Skewness might be limited if lizards pre-warm inside retreats before emerging in the morning, emerge only when operative temperatures are high enough to speed warming to activity Tb, or if cold lizards are especially wary and difficult to spot or catch. Telemetry studies may help evaluate these possibilities.

A single regression model is unlikely to hold throughout a large and complex spatial domain. A finite mixture of regression models can address this issue by clustering the data and assigning a regression model to explain each homogenous group. However, a typical finite mixture of regressions does not account for spatial dependencies. Furthermore, the number of components selected can be too high in the presence of skewed data and/or heavy tails. Here, we propose a mixture of regression models on a Markov random field with skewed distributions. The proposed model identifies the locations wherein the relationship between the predictors and the response is similar and estimates the model within each group as well as the number of groups. Overfitting is addressed by using skewed distributions, such as the skew-t or normal inverse Gaussian, in the error term of each regression model. Model estimation is carried out using an EM algorithm, and the performance of the estimators and model selection are illustrated through an extensive simulation study and two case studies.

This digital-map data set consists of a grid of generalized skew coefficients of logarithms of annual maximum streamflow for Oklahoma streams less than or equal to 2,510 square miles in drainage area. This grid of skew coefficients is taken from figure 11 of the Tortorelli and Bergman, 1985 report, "Techniques for estimating flood peak discharges for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma," U.S. Geological Survey Water-Resources Investigations Report 84-4358. The skew coefficients were used to develop peak-flow regression equations in the Tortorelli, 1997 report, "Techniques for estimating peak-streamflow frequency for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma," U.S. Geological Survey Water-Resources Investigations Report 97-4202. Only skew coefficient values within Oklahoma are intended for use with the regression equations. To save disk space, the skew coefficient values have been multiplied by 100 and rounded to integers with two significant digits.

This dataset contains upper air Skew-T Log-P charts taken at Boise, Idaho during the ICE-L project. The imagery are in GIF format. The imagery cover the time span from 2007-11-08 12:00:00 to 2008-01-03 12:00:00.

Both simulated and observed skews are given in the sense direction.

To improve flood-frequency estimates at rural streams in Mississippi, annual exceedance probability (AEP) flows at gaged streams in Mississippi and regional-regression equations, used to estimate annual exceedance probability flows for ungaged streams in Mississippi, were developed by using current geospatial data, additional statistical methods, and annual peak-flow data through the 2013 water year. The regional-regression equations were derived from statistical analyses of peak-flow data, basin characteristics associated with 281 streamgages, the generalized skew from Bulletin 17B (Interagency Advisory Committee on Water Data, 1982), and a newly developed study-specific skew for select four-digit hydrologic unit code (HUC4) watersheds in Mississippi. Four flood regions were identified based on residuals from the regional-regression analyses. No analysis was conducted for streams in the Mississippi Alluvial Plain flood region because of a lack of long-term streamflow data and poorly defined basin characteristics. Flood regions containing sites with similar basin and climatic characteristics yielded better regional-regression equations with lower error percentages. The generalized least squares method was used to develop the final regression models for each flood region for annual exceedance probability flows. The peak-flow statistics were estimated by fitting a log-Pearson type III distribution to records of annual peak flows and then applying two additional statistical methods: (1) the expected moments algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record; and (2) the generalized multiple Grubbs-Beck test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Standard errors of prediction of the generalized least-squares models ranged from 28 to 46 percent. Pseudo coefficients of determination of the models ranged from 91 to 96 percent. Flood Region A, located in north-central Mississippi, contained 27 streamgages with drainage areas that ranged from 1.41 to 612 square miles. The 1% annual exceedance probability had a standard error of prediction of 31 percent which was lower than the prediction errors in Flood Regions B and C.

This dataset contains Skew-T plots of dropsondes from the NOAA N42 P-3. Images exists for hurricanes Ophelia on September 7, 9, 11, 12, 16, and 17, and Rita on September 22-23.

This project examines whether people have an intrinsic preference for negatively skewed or positively skewed information structures and how these preferences relate to intrinsic preferences for informativeness. It reports results from 5 studies (3 lab experiments, 2 online studies).

Version 1

ARC-AGI Tasks where the job is to apply skew/unkew in the directions up/down/left/right. example count: 2-4. test count: 1-2. image size: 1-4.

  Version 2

image size: 1-7.

  Version 3

Earlier predictions added to some of the rows.

  Version 4

Added fields: arc_task, test_index, earlier_output.

  Version 5

Replaced RLE compressed response with raw pixel response.

  Version 6

image size: 1-9.

  Version 7

Smaller images… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-solve-skew-v8.

Skewed Cat

## Overview

Skewed Cat is a dataset for object detection tasks - it contains Cat annotations for 282 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains upper air Skew-T Log-P data collected at Denver during the HIPPO-2 project. The imagery are in GIF format. The imagery cover the time span from 2009-10-09 00:00:00 to 2010-01-04 00:00:00.

This dataset contains upper air Skew-T Log-P charts taken at Grand Junction, Colorado during the ICE-L project. The imagery are in GIF format. The imagery cover the time span from 2007-10-24 12:00:00 to 2008-01-03 12:00:00.

This repository contains raw data files and base codes to analyze them.A. The 'powerx_y.xlsx' files are the data files with the one dimensional trajectory of optically trapped probes modulated by an Ornstein-Uhlenbeck noise of given 'x' amplitude. For the corresponding diffusion amplitude A=0.1X(0.6X10-6)2 m2/s, x is labelled as '1'B. The codes are of three types. The skewness codes are used to calculate the skewness of the trajectory. The error_in_fit codes are used to calculate deviations from arcsine behavior. The sigma_exp codes point to the deviation of the mean from 0.5. All the codes are written three times to look ar T+, Tlast and Tmax.C. More information can be found in the manuscript.

This dataset contains upper air Skew-T Log-P charts taken at Denver, Colorado during the ICE-L project. The imagery are in GIF format. The imagery cover the time span from 2007-10-24 12:00:00 to 2008-01-03 12:00:00.