Statistics for the Life Sciences

✍ Scribed by Myra L. Samuels, Jeffrey A. Witmer, Andrew A. Schaffner

• Publisher: Pearson
• Year: 2016
• Tongue: English
• Leaves: 652
• Edition: 5th
• Category: Library

✦ Synopsis

Statistics for the Life Sciences is an introductory text in statistics, specifically addressed
to students specializing in the life sciences. Its primary aims are (1) to show students
how statistical reasoning is used in biological, medical, and agricultural research;
(2) to enable students to confidently carry out simple statistical analyses and to interpret
the results; and (3) to raise students’ awareness of basic statistical issues such as
randomization, confounding, and the role of independent replication.

✦ Table of Contents

Unit I: Data and Distributions
Chapter 1: Introduction. The nature and impact of variability in biological data. The
hazards of observational studies, in contrast with experiments. Random sampling.
Chapter 2: Description of distributions. Frequency distributions, descriptive statistics,
the concept of population versus sample.
Chapters 3, 4, and 5: Theoretical preparation. Probability, binomial and normal distributions,
sampling distributions.
Unit II: Inference for Means
Chapter 6: Confidence intervals for a single mean and for a difference in means.
Chapter 7: Hypothesis testing, with emphasis on the t test. The randomization test,
the Wilcoxon-Mann-Whitney test.
Chapter 8: Inference for paired samples. Confidence interval, t test, sign test, and
Wilcoxon signed-rank test.
Unit III: Inference for Categorical Data
Chapter 9: Inference for a single proportion. Confidence intervals and the chisquare
goodness-of-fit test.
Chapter 10: Relationships in categorical data. Conditional probability, contingency
tables. Optional sections cover Fisher’s exact test, McNemar’s test, and odds ratios.
Unit IV: Modeling Relationships
Chapter 11: Analysis of variance. One-way layout, multiple comparison procedures,
one-way blocked ANOVA, two-way ANOVA. Contrasts and multiple comparisons
are included in optional sections.
Chapter 12: Correlation and regression. Descriptive and inferential aspects of correlation
and simple linear regression and the relationship between them.
Chapter 13: A summary of inference methods.