The Practice of Statistics in the Life Sciences gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health. Based on David Moore’s The Basic Practice of Statistics, PSLS mirrors that #1 bestseller’s signature emphasis on statistical thinking, real data, and what statisticians actually do. 

Brigitte Baldi is a graduate of France’s Ecole Normale Supérieure in Paris. In her academic studies, she combined a love of math and quantitative analysis with wide interests in the life sciences. She studied math and biology in a double major and obtained a Masters in molecular biology and biochemistry and a Masters in cognitive sciences. She earned her Ph.D. in neuroscience from the Université Paris VI studying multisensory integration in the brain and used computer simulations to study patterns of brain reorganization after lesion as a post-doctoral fellow at the California Institute of Technology. She then worked as a management consultant advising corporations before returning to academia to teach statistics.

Dr. Baldi is currently a lecturer in the Department of Statistics at the University of California, Irvine. She is actively involved in statistical education. She was a local and later national advisor in the development of the statistics telecourse Statistically Speaking, replacing David Moore’s earlier telecourse Against All Odds. She developed UCI’s first online statistics courses and is interested in ways to integrate new technologies in the classroom to enhance participation and learning. She is currently serving as an elected member to the Executive Committee At Large of the section on Statistical Education of the American Statistical Association.

David S. Moore is Shanti S. Gupta Distinguished Professor of Statistics, Emeritus, at Purdue University and was 1998 president of the American Statistical Association. He received his A.B. from Princeton and his Ph.D. from Cornell, both in mathematics. He has written many research papers in statistical theory and served on the editorial boards of several major journals. Professor Moore is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He has served as program director for statistics and probability at the National Science Foundation. In recent years, Professor Moore has devoted his attention to the teaching of statistics. He was the content developer for the Annenberg/Corporation for Public Broadcasting college-level telecourse Against All Odds: Inside Statistics and for the series of video modules Statistics: Decisions through Data, intended to aid the teaching of statistics in schools. He is the author of influential articles on statistics education and of several leading texts. Professor Moore has served as president of the International Association for Statistical Education and has received the Mathematical Association of America’s national award for distinguished college or university teaching of mathematics.

Part I: Collecting and Exploring Data

Chapter 1 Picturing Distributions with Graphs

Individuals and variables

Identifying categorical and quantitative variables

Categorical variables: pie charts and bar graphs

Quantitative variables: histograms

Interpreting histograms

Quantitative variables: dotplots

Time plots

Discussion: (Mis)adventures in data entry

Chapter 2 Describing Quantitative Distributions with Numbers

Measures of center: median, mean

Measures of spread: percentiles, standard deviation

Graphical displays of numerical summaries

Spotting suspected outliers*

Discussion: Dealing with outliers

Organizing a statistical problem

Chapter 3 Scatterplots and Correlation

Explanatory and response variables

Relationship between two quantitative variables: scatterplots

Adding categorical variables to scatterplots

Measuring linear association: correlation

Chapter 4 Regression

The least-squares regression line

Facts about least-squares regression

Outliers and influential observations

Working with logarithm transformations*

Cautions about correlation and regression

Association does not imply causation

Chapter 5 Two-Way Tables

Marginal distributions

Conditional distributions

Simpsons paradox

Chapter 6 Samples and Observational Studies

Observation versus experiment

Sampling

Sampling designs

Sample surveys

Cohorts and case-control studies

Chapter 7 Designing Experiments

Designing experiments

Randomized comparative experiments

Common experimental designs

Cautions about experimentation

Ethics in experimentation

Discussion: The Tuskegee syphilis study

Chapter 8 Collecting and Exploring Data: Part I Review

Part I Summary

Comprehensive Review Exercises

Large Dataset Exercises

Online Data Sources

EESEE Case Studies

Part II: From Chance to Inference

Chapter 9 Essential Probability Rules

The idea of probability

Probability models

Probability rules

Discrete versus continuous probability models

Random variables

Risk and odds*

Chapter 10 Independence and Conditional Probabilities*

Relationships among several events

Conditional probability

General probability rules

Tree diagrams

Bayess theorem

Discussion: Making sense of conditional probabilities in diagnostic tests

Chapter 11 The Normal Distributions

Normal distributions

The 68-95-99.7 rule

The standard Normal distribution

Finding Normal probabilities

Finding percentiles

Using the standard Normal table*

Normal quantile plots*

Chapter 12 Discrete Probability Distributions*

The binomial setting and binomial distributions

Binomial probabilities

Binomial mean and standard deviation

The Normal approximation to binomial distributions

The Poisson distributions

Poisson probabilities

Chapter 13 Sampling Distributions

Parameters and statistics

Statistical estimation and sampling distributions

The sampling distribution of

The central limit theorem

The sampling distribution of

The law of large numbers*

Chapter 14 Introduction to Inference

Statistical estimation

Margin of error and confidence level

Confidence intervals for the mean

Hypothesis testing

P-value and statistical significance

Tests for a population mean

Tests from confidence intervals

Chapter 15 Inference in Practice

Conditions for inference in practice

How confidence intervals behave

How hypothesis tests behave

Discussion: The scientific approach

Planning studies: selecting an appropriate sample size

Chapter 16 From Chance to Inference: Part II Review

Part II Summary

Comprehensive Review Exercises

Advanced Topics (Optional Material)

Online Data Sources

EESEE Case Studies

Part III: Statistical Inference

Chapter 17 Inference about a Population Mean

Conditions for inference

The t distributions

The one-sample t confidence interval

The one-sample t test

Matched pairs t procedures

Robustness of t procedures

Chapter 18 Comparing Two Means

Comparing two population means

Two-sample t procedures

Robustness again

Avoid the pooled two-sample t procedures*

Avoid inference about standard deviations*

Chapter 19 Inference about a Population Proportion

The sample proportion

Large-sample confidence intervals for a proportion

Accurate confidence intervals for a proportion

Choosing the sample size*

Hypothesis tests for a proportion

Chapter 20 Comparing Two Proportions

Two-sample problems: proportions

The sampling distribution of a difference between proportions

Large-sample confidence intervals for comparing proportions

Accurate confidence intervals for comparing proportions

Hypothesis tests for comparing proportions

Relative risk and odds ratio*

Discussion: Assessing and understanding health risks

Chapter 21 The Chi-Square Test for Goodness of Fit

Hypotheses for goodness of fit

The chi-square test for goodness of fit

Interpreting chi-square results

Conditions for the chi-square test

The chi-square distributions

The chi-square test and the one-sample z test*

Chapter 22 The Chi-Square Test for Two-Way Tables

Two-way tables

The problem of multiple comparisons

Expected counts in two-way tables

The chi-square test

Conditions for the chi-square test

Uses of the chi-square test

Using a table of critical values*

The chi-square test and the two-sample z test*

Chapter 23 Inference for Regression

Conditions for regression inference

Estimating the parameters

Testing the hypothesis of no linear relationship

Testing lack of correlation*

Confidence intervals for the regression slope

Inference about prediction

Checking the conditions for inference

Chapter 24 One-Way Analysis of Variance: Comparing Several Means

Comparing several means

The analysis of variance F test

The idea of analysis of variance

Conditions for ANOVA

F distributions and degrees of freedom

The one-way ANOVA and the pooled two-sample t test*

Details of ANOVA calculations*

Chapter 25 Statistical Inference: Part III Review

Part III Summary

Review Exercises

Supplementary Exercises

EESEE Case Studies

Part IV: Optional Companion Chapters

Chapter 26 More about Analysis of Variance: Follow-up Tests and Two-Way ANOVA

Beyond one-way ANOVA

Follow up analysis: Tukey’s pairwise multiple comparisons

Follow up analysis: contrasts*

Two-way ANOVA: conditions, main effects, and interaction

Inference for two-way ANOVA

Some details of two-way ANOVA*

Chapter 27 Nonparametric Tests

Comparing two samples: the Wilcoxon rank sum test

Matched pairs: the Wilcoxon signed rank test

Comparing several samples: the Kruskal-Wallis test

Chapter 28 Multiple and Logistic Regression

Parallel regression lines

Estimating parameters

Conditions for inference

Inference for multiple regression

Interaction

A case study for multiple regression

Logistic regression

Inference for logistic regression

Notes and Data Sources

Tables

Answers to Selected Exercises

Some Data Sets Recurring Across Chapters

Index