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.

description: This Atlas presents more than 80,000 plots of the empirical frequency distributions of temperature and salinity for each 5-degree square area of the North Atlantic Ocean (80N to 30S) at all standard depth levels based on World Ocean Database 1998 data. Additional empirical statistical plots include the mean and standard deviation based on the arithmetic mean, the median and Median Absolute Deviation (MAD), winsorized estimates of the mean and standard deviation, quartiles, and skewness estimated from the quartiles. Some of these statistics are presented in both "normalized" and "natural" coordinates. Disc 1 contains seasonal distributions for the upper (0 m to 400 m) ocean. Disc 2 contains annual distributions for the deep (500 m - 5500 m) ocean. Copies of these discs are no longer available from the NODC Online Store.; abstract: This Atlas presents more than 80,000 plots of the empirical frequency distributions of temperature and salinity for each 5-degree square area of the North Atlantic Ocean (80N to 30S) at all standard depth levels based on World Ocean Database 1998 data. Additional empirical statistical plots include the mean and standard deviation based on the arithmetic mean, the median and Median Absolute Deviation (MAD), winsorized estimates of the mean and standard deviation, quartiles, and skewness estimated from the quartiles. Some of these statistics are presented in both "normalized" and "natural" coordinates. Disc 1 contains seasonal distributions for the upper (0 m to 400 m) ocean. Disc 2 contains annual distributions for the deep (500 m - 5500 m) ocean. Copies of these discs are no longer available from the NODC Online Store.

Annual descriptive price statistics for each calendar year 2005 – 2023 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

This dataset provides information about earnings of employees who are working in an area, who are on adult rates and whose pay for the survey pay-period was not affected by absence.

Tables provided here include total gross weekly earnings, and full time weekly earnings with breakdowns by gender, and annual median, mean and lower quartile earnings by borough and UK region. These are provided both in nominal and real terms.

Real earnings figures are on sheets labelled "real", are in 2016 prices, and calculated by applying ONS’s annual CPI index series for April to ASHE data.

Annual Survey of Hours and Earnings (ASHE) is based on a sample of employee jobs taken from HM Revenue & Customs PAYE records. Information on earnings and hours is obtained in confidence from employers. ASHE does not cover the self-employed nor does it cover employees not paid during the reference period.

The earnings information presented relates to gross pay before tax, National Insurance or other deductions, and excludes payments in kind.

The confidence figure is the coefficient of variation (CV) of that estimate. The CV is the ratio of the standard error of an estimate to the estimate itself and is expressed as a percentage. The smaller the coefficient of variation the greater the accuracy of the estimate. The true value is likely to lie within +/- twice the CV.

Results for 2003 and earlier exclude supplementary surveys. In 2006 there were a number of methodological changes made. For further details goto : http://www.nomisweb.co.uk/articles/341.aspx.
The headline statistics for ASHE are based on the median rather than the mean. The median is the value below which 50 per cent of employees fall. It is ONS's preferred measure of average earnings as it is less affected by a relatively small number of very high earners and the skewed distribution of earnings. It therefore gives a better indication of typical pay than the mean.

Survey data from a sample frame, use caution if using for performance measurement and trend analysis
'#' These figures are suppressed as statistically unreliable.
! Estimate and confidence interval not available since the group sample size is zero or disclosive (0-2).

Furthermore, data from Abstract of Regional Statistics, New Earnings Survey and ASHE have been combined to create long run historical series of full-time weekly earnings data for London and Great Britain, stretching back to 1965, and is broken down by sex.

Descriptive statistics of the German version of the Exergame Enjoyment Questionnaire (EEQ-G) in each condition and the reference questionnaires.

Extreme storm surges can overwhelm many coastal flooding protection measures in place and cause severe damages to private communities, public infrastructure, and natural ecosystems. In the US Mid-Atlantic, a highly developed and commercially active region, coastal flooding is one of the most significant natural hazards and a year-round threat from both tropical and extra-tropical cyclones. Mean sea levels and high-tide flood frequency has increased significantly in recent years, and major storms are projected to increase into the foreseeable future. We estimate extreme surges using hourly water level data and harmonic analysis for 1980–2019 at 12 NOAA tide gauges in and around the Delaware and Chesapeake Bays. Return levels (RLs) are computed for 1.1, 3, 5, 10, 25, 50, and 100-year return periods using stationary extreme value analysis on detrended skew surges. Two traditional approaches are investigated, Block Maxima fit to General Extreme Value distribution and Points-Over-Threshold fit to Generalized Pareto distribution, although with two important enhancements. First, the GEV r-largest order statistics distribution is used; a modified version of the GEV distribution that allows for multiple maximum values per year. Second, a systematic procedure is used to select the optimum value for r (for the BM/GEVr approach) and the threshold (for the POT/GP approach) at each tide gauge separately. RLs have similar magnitudes and spatial patterns from both methods, with BM/GEVr resulting in generally larger 100-year and smaller 1.1-year RLs. Maximum values are found at the Lewes (Delaware Bay) and Sewells Point (Chesapeake Bay) tide gauges, both located in the southwest region of their respective bays. Minimum values are found toward the central bay regions. In the Delaware Bay, the POT/GP approach is consistent and results in narrower uncertainty bands whereas the results are mixed for the Chesapeake. Results from this study aim to increase reliability of projections of extreme water levels due to extreme storms and ultimately help in long-term planning of mitigation and implementation of adaptation measures.

At the end of 2022, the Gini coefficient of wealth in India stood at 82.5. This was a slight increase from previous years. The trend since 2000 shows rising inequalities among the Indian population. What is Gini coefficient of wealth? The Gini coefficient is a measure of wealth inequality. The coefficient of the Gini index ranges from 0 to 1 with 0 representing perfect equality and 1 representing perfect inequality. Wealth and income distribution and inequality can however vary greatly. In 2023, South Africa topped the list of the most unequal countries in the world in terms of income inequality.
Why do economic inequalities persist in India? By the end of 2022, the richest citizens in the country owned more than 40 percent of the country’s wealth. Asia’s two richest men Mukesh Ambani and Gautam Adani are Indians. The number of high-net-worth individuals has continuously increased over the last decades. While millions of people escaped poverty in the country in the last few years, the wealth distribution between rich and poor remains skewed. Crony capitalism and the accumulation of wealth through inheritance are some of the factors behind this widening gap.