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.

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.

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.

Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.

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

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

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.

Abstract The essence of statistics is the possibility of extracting relevant information from data collected on aspects of reality. For this, the data are transformed into tables, graphs, and abstract measures. The student's understanding of these transformations is crucial for learning. In this article, we make theoretical reflections on the actions of students and the role of concrete material manipulated as ostensible in the management and display of qualitative statistics that undergo transformations in different semiotic records. For that, it uses the Theory of Semiotic Representation Records and the Anthropological Theory of Didactics, the latter limited to its perspective of ostensive and non-ostensive objects. The weightings indicate that the use of ostensive is motivating in the context of teaching and learning. Thus, it is expected that the reflections made here will contribute to a better understanding and teaching of statistical concepts, as well as to conducting new research.

One of the first steps in a reference interview is determining what is it the user really wants or needs. In many cases, the question comes down to the unit of analysis: what is it that is being investigated or researched? This presentation will take us through the concept of the unit of analysis so that we can improve our reference service — and make our lives easier as a result! Note: This presentation precedes Working with Complex Surveys: Canadian Travel Survey by Chuck Humphrey (14-Mar-2002).

Summary statistics are fundamental to data science, and are the buidling blocks of statistical reasoning. Most of the data and statistics made available on government web sites are aggregate, however, until now, we have not had a suitable linked data representation available. We propose a way to express summary statistics across aggregate groups as linked data using Web Ontology Language (OWL) Class based sets, where members of the set contribute to the overall aggregate value. Additionally, many clinical studies in the biomedical field rely on demographic summaries of their study cohorts and the patients assigned to each arm. While most data query languages, including SPARQL, allow for computation of summary statistics, they do not provide a way to integrate those values back into the RDF graphs they were computed from. We represent this knowledge, that would otherwise be lost, through the use of OWL 2 punning semantics, the expression of aggregate grouping criteria as OWL classes with variables, and constructs from the Semanticscience Integrated Ontology (SIO), and the World Wide Web Consortium's provenance ontology, PROV-O, providing interoperable representations that are well supported across the web of Linked Data. We evaluate these semantics using a Resource Description Framework (RDF) representation of patient case information from the Genomic Data Commons, a data portal from the National Cancer Institute.

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.

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

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.

The AI Presentation Tools market is rapidly evolving, reflecting a significant transformation in how businesses and individuals create, deliver, and engage with presentations. As organizations strive for efficiency and creativity in their communication, AI-powered presentation tools have emerged as a vital solution.

This presentation involves simulation and data generation processes, data analysis, and evaluation of classical and proposed methods of ordinal data analysis. All the parameters and metrics used are based on the methodology presented in the article titled "Statistical Mirroring-Based Ordinalysis: A Sensitive, Robust, Efficient, and Ordinality-Preserving Descriptive Method for Analyzing Ordinal Assessment Data," authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the paper's publication submitted to MethodsX Elsevier Publishing.

The validation process of ordinal data analysis methods (estimators) has the following specifications: 

• Simulation process: Monte Carlo simulation. • Data generation distributions: categorical, normal, and multivariate model distributions. • Data analysis: - Classical estimators: sum, average, and median ordinal score. - Proposed estimators: Kabirian coefficient of proximity, probability of proximity, probability of deviation.
• Evaluation metrics: - Overall estimates average. - Overall estimates median. - Efficiency (by statistical absolute meanic deviation method). - Sensitivity (by entropy method). - Normality, Mann-Whitney-U test, and others.

The Video Presentation Software market has witnessed remarkable growth in recent years, fueled by the increasing demand for dynamic, engaging content across various industries. As organizations shift from traditional presentation methods to more innovative and visually appealing formats, video presentation software

Home Office response to the consultation on the proposed changes to collection of recorded crime statistics. This summarises the response from users to the consultation and the subsequent advice the Crime Statistics Advisory Committee gave to the Home Secretary on the issue. The outlined changes to the classifications used for the collection of police recorded crime will come into effect on 1 April 2012.

United States Imports: 2-Digit: JP: Coin, Incl Gold Coin, Proof & Presentation data was reported at 0.000 USD mn in May 2018. This stayed constant from the previous number of 0.000 USD mn for Apr 2018. United States Imports: 2-Digit: JP: Coin, Incl Gold Coin, Proof & Presentation data is updated monthly, averaging 0.000 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 1.008 USD mn in Jun 1998 and a record low of 0.000 USD mn in May 2018. United States Imports: 2-Digit: JP: Coin, Incl Gold Coin, Proof & Presentation data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA094: Trade Statistics: Japan: Imports: Customs: SITC.