Role of Statistics for Decision-Making in Biostatistics

Thomas W. MacFarland
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Thomas W. MacFarland: Nova Southeastern University, Office of Institutional Effectiveness and College of Computing and Engineering

Chapter Chapter 3 in Introduction to Data Science in Biostatistics, 2024, pp 147-173 from Springer

Abstract: Abstract The purpose of this lesson is to outline a general process for the use of statistics in support of the decision-making process for biostatistics, where: Data scientists investigate problems, either on their own initiative or by assignment from others. Data scientists then develop action plans (e.g., workflow) on the many ways identified problems can are addressed, keeping in mind timelines, access to human and other resources, and, of course, budgets. Data scientists then engage in planned investigations, with actions ranging from data acquisition, data entry and organization, quality assurance checks of the data, preparation of syntax needed for statistical analysis of the data, etc. Data scientists then use syntax to put statistical output into tables and graphics, and through an iterative process with likely revisions, eventually reach closure to planned statistical analyses. Data scientists communicate outcomes with others, with structure and detail of the communication dependent on findings and intended audience. In all, data scientists use their skills and expertise in statistics to investigate problems, with focus in this text on problems associated with the biological sciences using R. As shown in the ending materials for this specific lesson, speculation and conjecture are put aside and data are then subjected to statistical analysis so that outcomes are confirmed and then communicated with others, whether deans and supervisors, peer scientists, business leaders, policymakers, government officials, and the public.

Keywords: Application Programming Interface (API); Association; Base R; Beautiful graphics; COVID-19; Chi-Square; Correlation; Friedman Twoway Analysis of Variance; Kruskal-Wallis Oneway Analysis of Variance; Long data; Mann-Whitney U Test; Nonparametric; Oneway Analysis of Variance; Parametric; Pearson Product-Moment Correlation Coefficient; Predictive modeling; R; Sign Test; Spearman Rank Correlation Coefficient; Student’s t-Test for Independent Samples; Student’s t-Test for Matched Pairs; Tidyverse ecosystem; Twoway Analysis of Variance; Wide data; Wilcoxon Matched-Pairs Signed-Ranks Test; Workflow (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46383-9_3

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