Introductory Statistics for the Health Sciences (Hardback) book cover

Introductory Statistics for the Health Sciences

By Lise DeShea, Larry E. Toothaker

© 2015 – Chapman and Hall/CRC

603 pages | 151 Color Illus.

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Introductory Statistics for the Health Sciences takes students on a journey to a wilderness where science explores the unknown, providing students with a strong, practical foundation in statistics. Using a color format throughout, the book contains engaging figures that illustrate real data sets from published research. Examples come from many areas of the health sciences, including medicine, nursing, pharmacy, dentistry, and physical therapy, but are understandable to students in any field. The book can be used in a first-semester course in a health sciences program or in a service course for undergraduate students who plan to enter a health sciences program.

The book begins by explaining the research context for statistics in the health sciences, which provides students with a framework for understanding why they need statistics as well as a foundation for the remainder of the text. It emphasizes kinds of variables and their relationships throughout, giving a substantive context for descriptive statistics, graphs, probability, inferential statistics, and interval estimation. The final chapter organizes the statistical procedures in a decision tree and leads students through a process of assessing research scenarios.

Web Resource

The authors have partnered with William Howard Beasley, who created the illustrations in the book, to offer all of the data sets, graphs, and graphing code in an online data repository via GitHub. A dedicated website gives information about the data sets and the authors’ electronic flashcards for iOS and Android devices. These flashcards help students learn new terms and concepts.


"… well-written and easy to understand … the examples and exercises are very nicely done. I found myself wanting to work through all of them! … the figures and tables are visually appealing and useful. … many students in the health sciences will find the text to be interesting and useful.

I think that the material is perfect for those who are math-phobic and those who have not taken any math classes in several years. … The figures illustrate the concepts well, and the practice scenarios are very helpful for students to assess what they have learned …"

—Robert A. Oster, PhD, Department of Medicine, University of Alabama at Birmingham, and Former Chair and Program Chair of the American Statistical Association Section on Teaching of Statistics in the Health Sciences

Table of Contents

The Frontier between Knowledge and Ignorance


The Context for Statistics: Science and Research

Definition of Statistics

The Big Picture: Populations, Samples, and Variables

Generalizing from the Sample to the Population

Experimental Research

Blinding and Randomized Block Design

Nonexperimental Research

Quasi-Experimental Research

Inferences and Kinds of Validity

Describing Distributions with Statistics: Middle, Spread, and Skewness


Measures of Location

Measures of Spread or Variability

Measure of Skewness or Departure from Symmetry

Exploring Data Visually


Why Graph Our Data?

Pie Charts and Bar Graphs

Two Kinds of Dot Plots



Time Plots (Line Graphs)


Graphs Can Be Misleading

Beyond These Graphs

Relative Location and Normal Distributions


Standardizing Scores

Computing a z Score in a Sample

Computing a z Score in a Population

Comparing z Scores for Different Variables

A Different Kind of Standard Score

Distributions and Proportions

Areas under the Standard Normal Curve

Bivariate Correlation


Pearson’s Correlation Coefficient

Verbal Definition of Pearson’s r

Judging the Strength of a Correlation

What Most Introductory Statistics Texts Say about Correlation

Pearson’s r Measures Linear Relationships Only

Correlations Can Be Influenced by Outliers

Correlations and Restriction of Range

Combining Groups of Scores Can Affect Correlations

Missing Data Are Omitted from Correlations

Pearson’s r Does Not Specify Which Variable Is the Predictor

Probability and Risk


Relative Frequency of Occurrence

Conditional Probability

Special Names for Certain Conditional Probabilities

Statistics Often Accompanying Sensitivity and Specificity

Two Other Probabilities: "And" and "Or"

Risk and Relative Risk

Other Statistics Associated with Probability

Sampling Distributions and Estimation


Quantifying Variability from Sample to Sample

Kinds of Distributions

Why We Need Sampling Distributions

Comparing Three Distributions: What We Know So Far

Central Limit Theorem

Unbiased Estimators

Standardizing the Sample Mean

Interval Estimation

Calculating a Confidence Interval Estimate of μ

Hypothesis Testing and Interval Estimation


Testable Guesses

The Rat Shipment Story

Overview of Hypothesis Testing

Two Competing Statements about What May Be True

Writing Statistical Hypotheses

Directional and Nondirectional Alternative Hypotheses

Choosing a Small Probability as a Standard

Compute the Test Statistic and a Certain Probability

Decision Rules When H1 Predicts a Direction

Decision Rules When H1 Is Nondirectional


Testing Hypotheses with Confidence Intervals: Nondirectional H1

Testing Hypotheses with Confidence Intervals: Directional H1

Types of Errors and Power


Possible Errors in Hypothesis Testing

Probability of a Type I Error

Probability of Correctly Retaining the Null Hypothesis

Type I Errors and Confidence Intervals

Probability of a Type II Error and Power

Factors Influencing Power: Effect Size

Factors Influencing Power: Sample Size

Factors Influencing Power: Directional Alternative Hypotheses

Factors Influencing Power: Significance Level

Factors Influencing Power: Variability

Factors Influencing Power: Relation to Confidence Intervals

One-Sample Tests and Estimates


One-Sample t Test

Distribution for Critical Values and p Values

Critical Values for the One-Sample t Test

Completing the Sleep Quality Example


Confidence Interval for μ Using One-Sample t Critical Value

Graphing Confidence Intervals and Sample Means

Two-Sample Tests and Estimates


Pairs of Scores and the Paired t Test

Two Other Ways of Getting Pairs of Scores

Fun Fact Associated with Paired Means

Paired t Hypotheses When Direction Is Not Predicted

Paired t Hypotheses When Direction Is Predicted

Formula for the Paired t Test

Confidence Interval for the Difference in Paired Means

Comparing Means of Two Independent Groups

Independent t Hypotheses When Direction Is Not Predicted

Independent t Hypotheses When Direction Is Predicted

Formula for the Independent-Samples t Test


Confidence Intervals for a Difference in Independent Means

Limitations on Using the t Statistics in This Chapter

Tests and Estimates for Two or More Samples


Going beyond the Independent-Samples t Test

Variance between Groups and Within Groups

One-Way ANOVA F Test: Logic and Hypotheses

Computing the One-Way ANOVA F Test

Critical Values and Decision Rules

Numeric Example of a One-Way ANOVA F Test

Testing the Null Hypothesis

Assumptions and Robustness

How to Tell Which Group Is Best

Multiple Comparison Procedures and Hypotheses

Many Statistics Possible for Multiple Comparisons

Confidence Intervals in a One-Way ANOVA Design

Tests and Estimates for Bivariate Linear Relationships


Hypothesizing about a Correlation

Testing a Null Hypothesis about a Correlation

Assumptions of Pearson’s r

Using a Straight Line for Prediction

Linear Regression Analysis

Determining the Best-Fitting Line

Hypothesis Testing in Bivariate Regression

Confidence Intervals in Simple Regression

Limitations on Using Regression

Analysis of Frequencies and Ranks


One-Sample Proportion

Confidence Interval for a Proportion

Goodness of Fit Hypotheses

Goodness of Fit Statistic

Computing the Chi-Square Test for Goodness of Fit

Goodness of Fit: Assumptions and Robustness

Chi-Square for Independence

Hypotheses for Chi-Square for Independence

Computing Chi-Square for Independence

Relative Risk

Odds Ratios

Analysis of Ranks

Choosing an Analysis Plan


Statistics That We Have Covered

Organizing Our List: Kind of Outcomes, Number of Samples

Adding to the Tree: Two Samples

Adding Again to the Tree: More Than Two Samples

Completing the Tree: Analysis of Categories

Completing the Tree: The Remaining Categorical Analyses

Suggested Answers to Odd-Numbered Exercises



References appear at the end of each chapter.

About the Authors

Lise DeShea is the senior research biostatistician in the College of Nursing at the University of Oklahoma Health Sciences Center. She has served on the faculty of the University of Kentucky and worked as a statistician for a Medicaid agency. She has conducted research on emergency room utilization, bootstrapping, and forgiveness.

Larry E. Toothaker is an emeritus David Ross Boyd Professor, the highest honor for teaching excellence at the University of Oklahoma. He has conducted research on multiple comparison procedures and nonparametric methods. He retired in 2008 after teaching statistics in the Department of Psychology for 40 years.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Biostatistics