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Biostatistics for the Biological and Health Sciences, 3rd edition

Published by Pearson (January 14, 2023) © 2024

  • Marc M. Triola NYU School of Medicine
  • Mario F. Triola Dutchess Community College
  • Jason Roy Rutgers University

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For Biostatistics courses taken by biology and health sciences majors.

Real applications bring theories and methods to life

Biostatistics for the Biological and Health Sciences is designed for a wide variety of science majors taking their first statistics course. Abundant examples, real data and a friendly writing style help students develop skills in critical thinking, technology and communication. This excellent introduction, from 2 biological sciences experts and the author of the #1 statistics book, is highly readable and relevant.

The 3rd Edition adds data sets, examples and exercises on interesting topics (such as COVID-19 clinical trials and tracking, biometrics security) and much more.

Hallmark features of this title

  • Carefully selected real data: 92% of examples and 92% of exercises are based on real data.
  • Margin essays illustrate uses and abuses of statistics in real, practical and interesting applications.
  • Flow charts throughout simplify and clarify more complex concepts and procedures.
  • Easy-to-assign exercises are graded by difficulty. Exercises that are particularly difficult or involve a new concept appear at the end of exercise sets and are marked by an asterisk for instructors' convenience.
  • End-of-chapter features include chapter reviews, review exercises, From Data to Decision: Critical Thinking capstone problems, group activities, and more.
  • The latest and best methods used by professional statisticians are incorporated.

New and updated features of this title

  • New exercises and examples: 54% of exercises and 58% of examples are new to the 3rd Edition.
  • New and updated real data sets throughout provide relevant, interesting statistical applications, such as COVID-19 clinical trials and tracking, biometric security, body measurements, brain sizes and IQ scores, and data on births. An expanded data set library in Appendix B provides ready access to real and interesting data (28 data sets, from 18 in the previous edition).
  • Larger data sets include 465,506; 70,942; 22,385; 6068; 5755; and 3982 cases. Working with such large data sets is essential in an age of big data and data science.
  • New exercise types: Cumulative Review Exercises near the end of Chapters 9, 10 and 11 include open-ended questions that present students with a data set, then ask them to pose a key question relevant to the data, identify a procedure for addressing that question, and analyze the data to form a conclusion.
  • New Big (or Very Large) Data Projects near the end of each chapter ask students to think critically while using large data sets.
  • A new Chapter Problem Icon highlights Examples that relate to the Chapter Problem, to show how different statistical concepts and procedures can be applied to the real-world issue highlighted in the chapter.

Features of MyLab Statistics for the 3rd Edition

  • Integrated Review: Integrated Review MyLab courses provide the full suite of supporting resources for the BioStatistics course, plus additional assignments and study aids from select topics for students who will benefit from remediation. Assignments for the integrated review content are preassigned in MyLab, making it easier than ever for instructors to create their courses.
  • BioStatistics in Practice videos: Marty Triola interviews professionals about the use of statistics in practice, providing students with useful context for the concepts they're learning.
  • The R Guidebook provides students with getting started instructions, step-by-step walkthroughs, and support for using R to perform data analysis with the examples in the text.
  • Animated Flowcharts provide narrated explanations of complex concepts and procedures. They are included with connected assessments in the item library and can be assigned as media.
  • Mindset videos and assignable, open-ended exercises foster a growth mindset and encourage students to maintain a positive attitude about learning, value their own ability to grow, and view mistakes as learning opportunities.
  • Personal Inventory Assessments are online exercises that promote self-reflection and include topics such as Stress Management and Time Management.

1. Introduction to Statistics

  • 1-1 Statistical and Critical Thinking
  • 1-2 Types of Data
  • 1-3 Collecting Sample Data
  • 1-4 Ethics in Statistics (download only)

2. Exploring Data with Tables and Graphs

  • 2-1 Frequency Distributions for Organizing and Summarizing Data
  • 2-2 Histograms
  • 2-3 Graphs That Enlighten and Graphs That Deceive
  • 2-4 Scatterplots, Correlation, and Regression

3. Describing, Exploring, and Comparing Data

  • 3-1 Measures of Center
  • 3-2 Measures of Variation
  • 3-3 Measures of Relative Standing and Boxplots

4. Probability

  • 4-1 Basic Concepts of Probability
  • 4-2 Addition Rule and Multiplication Rule
  • 4-3 Complements, Conditional Probability, and Bayes' Theorem
  • 4-4 Risks and Odds
  • 4-5 Rates of Mortality, Fertility, and Morbidity
  • 4-6 Counting

5. Discrete Probability Distributions

  • 5-1 Probability Distributions
  • 5-2 Binomial Probability Distributions
  • 5-3 Poisson Probability Distributions

6. Normal Probability Distributions

  • 6-1 The Standard Normal Distribution
  • 6-2 Real Applications of Normal Distributions
  • 6-3 Sampling Distributions and Estimators
  • 6-4 The Central Limit Theorem
  • 6-5 Assessing Normality
  • 6-6 Normal as Approximation to Binomial (download only)

7. Estimating Parameters and Determining Sample Sizes

  • 7-1 Estimating a Population Proportion
  • 7-2 Estimating a Population Mean
  • 7-3 Estimating a Population Standard Deviation or Variance
  • 7-4 Bootstrapping: Using Technology for Estimates

8. Hypothesis Testing

  • 8-1 Basics of Hypothesis Testing
  • 8-2 Testing a Claim About a Proportion
  • 8-3 Testing a Claim About a Mean
  • 8-4 Testing a Claim About a Standard Deviation or Variance
  • 8-5 Resampling: Using Technology for Hypothesis Testing<

9. Inferences from Two Samples

  • 9-1 Two Proportions
  • 9-2 Two Means: Independent Samples
  • 9-3 Matched Pairs
  • 9-4 Two Variances or Standard Deviations
  • 9-5 Resampling: Using Technology for Inferences

10. Correlation and Regression

  • 10-1 Correlation
  • 10-2 Regression
  • 10-3 Prediction Intervals and Variation
  • 10-4 Multiple Regression
  • 10-5 Dummy Variables and Logistic Regression

11. Goodness-of-Fit and Contingency Tables

  • 11-1 Goodness-of-Fit
  • 11-2 Contingency Tables

12. Analysis of Variance

  • 12-1 One-Way ANOVA
  • 12-2 Two-Way ANOVA

13. Nonparametric Tests

  • 13-1 Basics of Nonparametric Tests
  • 13-2 Sign Test
  • 13-3 Wilcoxon Signed-Ranks Test for Matched Pairs
  • 13-4 Wilcoxon Rank-Sum Test for Two Independent Samples
  • 13-5 Kruskal-Wallis Test for Three or More Samples
  • 13-6 Rank Correlation

14. Survival Analysis

  • 14-1 Life Tables
  • 14-2 Kaplan-Meier Survival Analysis

APPENDICES

A: Tables and Formulas
B: Data Sets
C: Websites and Bibliography of Books
D: Answers to Odd-Numbered Section Exercises (and all Quick Quizzes, all Review Exercises, and all Cumulative Review Exercises)
Subject Index

About our authors

Mark Triola, MD, FACP is the Associate Dean for Educational Informatics at NYU School of Medicine, the founding director of the NYU Langone Medical Center Institute for Innovations in Medical Education (IIME), and an Associate Professor of Medicine. Dr. Triola's research focuses on precision education and the use of AI tools to efficiently personalize medical education for individual learners and give new insights to their programs and coaches. His lab develops new learning technologies and AI-driven educational interventions and also works to define educationally sensitive patient and system outcomes that can be used to assess the impact of training. Dr. Triola and IIME have been funded by the National Institutes of Health, the Josiah Macy Jr. Foundation, the Department of Education, the Department of Defense, and the American Medical Association's Accelerating Change in Medical Education program.

Mario F. Triola is a Professor Emeritus of Mathematics at Dutchess Community College, where he has taught statistics for over 30 years. Marty is the author of Elementary Statistics, 14th Edition; Essentials of Statistics, 7th Edition; Elementary Statistics Using Excel, 7th Edition; and Elementary Statistics Using the TI-83/84 Plus Calculator, 5th Edition; and he is a co-author of Statistical Reasoning for Everyday Life, 5th Edition. Elementary Statistics is currently available as an International Edition, and it has been translated into several foreign languages. Marty designed the original Statdisk statistical software, and he has written several manuals and workbooks for technology supporting statistics education. He has been a speaker at many conferences and colleges.

Marty's consulting work includes the design of casino slot machines and the design of fishing rods, and he has worked with attorneys in determining probabilities in paternity lawsuits, analyzing data in medical malpractice lawsuits, identifying salary inequities based on gender, and analyzing disputed election results. He has also used statistical methods to analyze medical school surveys, survey results for the New York City Transit Authority, and COVID-19 virus data for government officials. Marty has testified as an expert witness in the New York State Supreme Court. As of this writing, Marty's Elementary Statistics has been the #1 statistics text in the United States for 27 consecutive years.

Jason Roy, PhD is a Professor of Biostatistics and Chair of the Department of Biostatistics and Epidemiology at Rutgers University. He is director of Rutgers University Biostatics and Epidemiology Services and co-director of Biostatistics, Epidemiology, and Research Design core, NJ ACTS. Previously, he was Professor of Biostatistics in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania. He received his PhD in Biostatistics in 2000 from the University of Michigan. He was the recipient of the 2002 David P. Byar Young Investigator Award from the American Statistical Association Biometrics Section. Dr. Roy is interested in methodological research in developing flexible Bayesian methods for large, observational studies, especially data from EHR and mobile health. He is particularly interested in causal inference problems, where Bayesian nonparametric methods can be used in conjunction with g-computation. He is also interested in functional clustering methods, which can be very useful for extracting features from intensively collected data (such as from mobile devices). Much of his collaborative research is in pharmacoepidemiology.

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