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.

Descriptive statistics for the presentations of shapes.

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.

Based on SC Broadband Office (SCBBO) analysis of FCC Broadband Data Collection (fcc.gov), Jun. 30, 2023 (as of Mar. 19, 2024), submissions that were audited through the SC BEAD Challenge process which concluded on Jun. 30, 2024. The SC BEAD Challenge process relied upon FCC BSL Fabric Jun. 30, 2023, Version 3.2 (pub. Jul. 21, 2023). Satellite and mobile broadband services are excluded. Population and K-12 estimates are derived from residential unit level data based on the FCC BSL fabric. Broadband investment data based on SCBBO actual BSL contract data in the case of state-managed funds (when available) and best-available federal data in the case of FCC and US Department of Agriculture (USDA) managed investments. County-level investments are based upon data provided to the SCBBO. The SCBBO is neither responsible nor liable for damages or injuries caused by failure of performance, error, omission, inaccuracy, inaccessibility, incompleteness or any other errors of this information period or formatting on this slide. This data should be used for general reference purposes only. Additional broadband information regarding South Carolina may be found at www.scdigitaldrive.org. Submit comments or questions to broadband@ors.sc.gov

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).

This statistic shows the average annual attendance of physical and digital informational presentations to groups at academic libraries in the United States in 2015, by type of degree granted. In 2015, academic libraries at post-secondary education institutions in the U.S. that granted doctoral degrees had an average annual attendance of ****** people for presentations.

Inferential statistics for Experiment 2 audience ratings.

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).

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 statistic shows the average number of physical and digital informational presentations to groups at academic libraries in the United States in 2015, by type of degree granted by institution. In 2015, academic libraries at post-secondary education institutions in the U.S. that granted doctoral degrees made *** physical informational presentations on average.

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.

Financial overview and grant giving statistics of Paradox Presentations

Financial overview and grant giving statistics of Mountain Top Presentations Inc.

According to a survey conducted in Australia in May 2020, ** percent of general practitioners reported psychological issues as the most commonly seen patient presentation during the COVID-19 pandemic in early 2020. The GPs reported a sharp increase in preventive healthcare patient presentation rising from ** percent in 2019 to ** percent in 2020.

Financial overview and grant giving statistics of Sandager Presentations

This statistic presents information on the truthfulness of online dating users in the United States when they presenting themselves on their dating profile. During an April 2017 survey, it was found that 60 percent of responding dating website or app users always presented themselves truthfully.

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