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© 2017

Translating Statistics to Make Decisions

A Guide for the Non-Statistician

Book

Table of contents

  1. Front Matter
    Pages i-xix
  2. Victoria Cox
    Pages 1-31
  3. Victoria Cox
    Pages 33-46
  4. Victoria Cox
    Pages 47-74
  5. Victoria Cox
    Pages 75-102
  6. Victoria Cox
    Pages 103-124
  7. Victoria Cox
    Pages 125-159
  8. Victoria Cox
    Pages 161-239
  9. Victoria Cox
    Pages 241-269
  10. Victoria Cox
    Pages 271-305
  11. Victoria Cox
    Pages 307-318
  12. Back Matter
    Pages 319-324

About this book

Introduction

Examine and solve the common misconceptions and fallacies that non-statisticians bring to their interpretation of statistical results. Explore the many pitfalls that non-statisticians—and also statisticians who present statistical reports to non-statisticians—must avoid if statistical results are to be correctly used for evidence-based business decision making.

Victoria Cox, senior statistician at the United Kingdom’s Defence Science and Technology Laboratory (Dstl), distills the lessons of her long experience presenting the actionable results of complex statistical studies to users of widely varying statistical sophistication across many disciplines: from scientists, engineers, analysts, and information technologists to executives, military personnel, project managers, and officials across UK government departments, industry, academia, and international partners.

The author shows how faulty statistical reasoning often undermines the utility of statistical results even among those with advanced technical training. Translating Statistics into Better Decisions teaches statistically naive readers enough about statistical questions, methods, models, assumptions, and statements that they will be able to extract the practical message from statistical reports and better constrain what conclusions cannot be made from the results. To non-statisticians with some statistical training, this book offers brush-ups, reminders, and tips for the proper use of statistics and solutions to common errors. To fellow statisticians, the author demonstrates how to present statistical output to non-statisticians to ensure that the statistical results are correctly understood and properly applied to real-world tasks and decisions. The book avoids algebra and proofs, but it does supply code written in R for those readers who are motivated to work out examples.

Pointing along the way to instructive examples of statistics gone awry, Translating Statistics into Better Decisions walks readers through the typical course of a statistical study, progressing from the experimental design stage through the data collection process, exploratory data analysis, descriptive statistics, uncertainty, hypothesis testing, statistical modelling and multivariate methods, to graphs suitable for final presentation. The steady focus throughout the book is on how to turn the mathematical artefacts and specialist jargon that are second nature to statisticians into plain English for corporate customers and stakeholders. The final chapter neatly summarizes the book’s lessons and insights for accurately communicating statistical reports to the non-statisticians who commission and act on them.

Readers will

• Recognize and avoid common errors and misconceptions that cause statistical studies to be misinterpreted and misused by non-statisticians in organizational settings

• Gain a practical understanding of the methods, processes, capabilities, and caveats of statistical studies to improve the application of statistical data to business decisions

• See how to code statistical solutions in R

Keywords

sample size DoE physical trials subjective trials data formatting exploratory data analysis outliers descriptive statistics location statistics confidence intervals tolerance intervals binary data hypothesis tests t-tests nonparametric equivalents proportion tests modeling multivariate analysis R

Authors and affiliations

  1. 1. DstlSalisburyUnited Kingdom

About the authors

Victoria Cox is Senior Statistician at the United Kingdom’s Defence Science and Technology Laboratory (Dstl), where she advises internal teams and external organizations on the experimental design of statistical studies and the application of the methods of statistical analysis to a wide variety of practical problems. She teaches statistical courses to non-statistician scientists, technologists, analysts, managers, and executives. Cox received her degree in mathematics from the University of Sheffield and studied at l’Ecole Nationale de la Statistique et de l’Analyse de l’Information (ENSAI).

Bibliographic information