Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research, and to express their degree of certainty about these quantities. In this article, the authors offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method, no matter how complicated, and to interpret and present it in a reader-friendly manner. Using this technique requires some sophistication, but its application should make the results of quantitative articles more informative and transparent to all. To illustrate their recommendations, the authors replicate the results of several published works, showing in each case how the authors' own conclusions could be expressed more sharply and informatively, and how this approach reveals important new information about the research questions at hand.

This statistic depicts the distribution of tools used to compile data and present analytics and/or reports to management, according to a marketing survey of C-level executives, conducted in ************* by Black Ink. As of *************, * percent of respondents used statistical modeling tools, such as IBM's SPSS or the SAS Institute's Statistical Analysis System package, to compile and present their reports.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed.

Social Scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Using this technique requires some expertise, which we try to provide herein, but its application should make the results of quantitative articles more informative and transparent. To illustrate our recommendations, we replicate the results of several published works, showing in each case how the authors' own concl usions can be expressed more sharply and informatively, and, without changing any data or statistical assumptions, how our approach reveals important new information about the research questions at hand. We also offer very easy-to-use Clarify software that implements our suggestions. See also: Unifying Statistical Analysis

Frequency (first word, second word, and phrase), Cloze probability, Familiarity, Concreteness, Difficulty, and Meaningfulness of 115 literary metaphors out of context. Minimum and maximum values, means, standard deviations, medians, α values, skewness values, and 95% confidence intervals are provided for each variable.aValues were log10 transformed.Descriptive statistics of Experiment 1 (out of context presentation).

Gulpease readability index, Cloze probability, Familiarity, Concreteness, Difficulty, and Meaningfulness of 65 literary metaphors presented in context. Minimum and maximum values, means, standard deviations, medians, α values, skewness values, and 95% confidence intervals are provided.Descriptive statistics of Experiment 2 (in context presentation).

Natural history museums often contain large collections of the same species and therefore, are a resource for studying intraspecific variation. This module uses 172 images of rock pocket mouse skulls from the UTEP Biodiversity Collections to introduce students to collecting data from images and principles of basic statistics. This module resource focuses on immersing students into the development of study design, analysis, discussion, and communication without overwhelming them. Students enter their data into a Google sheet app that combines data entry, statistical analysis, and presentation all in one. The collaborative framework asks students to work together, share resources, and develop their own questions while learning the principles behind taking measurements from images of museum specimens.

This dataset contains several files related to our research paper titled "Attention Allocation to Projection Level Alleviates Overconfidence in Situation Awareness". These files are intended to provide a comprehensive overview of the data analysis process and the presentation of results. Below is a list of the files included and a brief description of each:

R Scripts: These are scripts written in the R programming language for data processing and analysis. The scripts detail the steps for data cleaning, transformation, statistical analysis, and the visualization of results. To replicate the study findings or to conduct further analyses on the dataset, users should run these scripts.

R Markdown File: Offers a dynamic document that combines R code with rich text elements such as paragraphs, headings, and lists. This file is designed to explain the logic and steps of the analysis in detail, embedding R code chunks and the outcomes of code execution. It serves as a comprehensive guide to understanding the analytical process behind the study.

HTML File: Generated from the R Markdown file, this file provides an interactive report of the results that can be viewed in any standard web browser. For those interested in browsing the study's findings without delving into the specifics of the analysis, this HTML file is the most convenient option. It presents the final analysis outcomes in an intuitive and easily understandable manner. For optimal viewing, we recommend opening the HTML file with the latest version of Google Chrome or any other modern web browser. This approach ensures that all interactive functionalities are fully operational.

Together, these files form a complete framework for the research analysis, aimed at enhancing the transparency and reproducibility of the study.

Financial overview and grant giving statistics of Presentation Senior Community

Presentation on data developments and emerging projects regarding macroeconomic accounts, environment, business surveys, prices, cost-recovery projects, and statistical frameworks.

An overview of Statistics Canada social statistics with cannabis content

This is a collection of presentations given at the Population Statistics User Group hosted by the GLA City Intelligence and attended by representatives of London local authorities. Additional presentations will be added to this page over time. An archive of presentations delivered at conferences is available here.

Visual analytics: Exposing the past, understanding the present, and looking to the future Dan Ariely, founder of The Center for Advanced Hindsight once posted on Facebook, “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...” This is especially true in Higher Education as much of the work being done to organize, connect, and analyze big data is happening in the for profit sector. This multimedia presentation (video, photos, and text) has three goals. (1) Discuss how the field visual analytics is tackling the problem of analyzing big data. (2) Explore when visual analytics is superior and inferior to typical statistics. (3) Tactics and tools for Institutional Researchers to use in their everyday work to change data into actionable intelligence.

Question Paper Solutions of chapter Collection, Editing and Presentation of Data of Fundamentals of Statistics, 1st Semester , Bachelor of Business Administration

A paper outlining how the gender pay gap will be presented in future ONS Statistical Bulletins

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Presentation of the Gender Pay Gap: ONS Position Paper

The New York City Police Department records reported crime and offense data based upon the New York State Penal Law and other New York State laws. For statistical presentation purposes the numerous law categories and subsections are summarized by law class, Felony, Misdemeanor and Violation. The tabular data compiles reported crime and offense data recorded by the New York City Police Department. Separate tables are presented for the Seven Major Felonies, Non-Seven Major Felony Crimes, Misdemeanors and Violations.

This metric estimates the proportion of all malignant cancers where patients first presented to the health system as an emergency. This latest publication has been updated to include quarterly data for January to March, April to June and July to September 2022 (quarter 4 of financial year 2021 to 2022 and quarters 1 and 2 of financial year 2022 to 2023) and an update of the one-year rolling proportion.

This guidance note sets out the recommended standard presentation of statistics for Middle Layer and Lower Layer Super Output Areas nested within relevant local authority districts in the UK and is now available in accessible format (File Size - 2.6 MB).