Interpreting Statistical Findings

Part 1 Worked Examples
The randomised controlled trial
The Health survey
Part 2 Interpreting statistical concepts
Measuring variables: continuous, ordinal and categorical data
Describing continuous data: The normal distribution
Describing nonparametric data
Measuring concepts: Validity and reliability
Sampling data: Probability and non-probability samples
Sample size: criteria for judging adequacy
Testing hypotheses: what does p actually mean?
Part 3 Statistical tests
Introduction to inferential statistics
Comparing two independent (unrelated) groups: independent (unrelated) t test, Mann-Whitney U test, contingency analysis- Fisher's exact test and Chi-square test
Comparing three or more independent (unrelated) groups: One-way ANOVA, Kruskal Wallis test and Chi-square test
Comparing two sets of related data: Matched pairs or single-sample repeated measures- related (paired) t test, Wilcoxon signed rank test, sign test and McNemar's test
Complex group comparisons: ANOVA / ANCOVA, Friedman two-way ANOVA by ranks and Cochrane Q test
Simple tests of association: Correlation and linear regression
complex associations: Multiple and logistic regression
Part 4 Quick reference guide
I Framework for statistical review
II Glossary of terms
III Guide to statistical symbols
IV Overview of common statistical tests
V Guide to the assumptions that underpin statistical tests
VI Summary of statistical test selection and results
VII Extracts from statistical tables

Jan Walker is an experienced researcher, lecturer and supervisor. Her previous books include Psychology for Nurses and the Caring Professions 3/e (Open University Press, 2007).

Palo Almond is a lecturer in public health and primary health care and an experienced research supervisor. She has over 30 years of experience in nursing, public health and midwifery practice, research and education.