Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 ‘Doing science’ – hypotheses, experiments, and disproof
- 3 Collecting and displaying data
- 4 Introductory concepts of experimental design
- 5 Probability helps you make a decision about your results
- 6 Working from samples – data, populations, and statistics
- 7 Normal distributions – tests for comparing the means of one and two samples
- 7 Type 1 and Type 2 errors, power, and sample size
- 9 Single factor analysis of variance
- 10 Multiple comparisons after ANOVA
- 11 Two factor analysis of variance
- 12 Important assumptions of analysis of variance: transformations and a test for equality of variances
- 13 Two factor analysis of variance without replication, and nested analysis of variance
- 14 Relationships between variables: linear correlation and linear regression
- 15 Simple linear regression
- 16 Non-parametric statistics
- 17 Non-parametric tests for nominal scale data
- 18 Non-parametric tests for ratio, interval, or ordinal scale data
- 19 Choosing a test
- 20 Doing science responsibly and ethically
- References
- Index
17 - Non-parametric tests for nominal scale data
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 ‘Doing science’ – hypotheses, experiments, and disproof
- 3 Collecting and displaying data
- 4 Introductory concepts of experimental design
- 5 Probability helps you make a decision about your results
- 6 Working from samples – data, populations, and statistics
- 7 Normal distributions – tests for comparing the means of one and two samples
- 7 Type 1 and Type 2 errors, power, and sample size
- 9 Single factor analysis of variance
- 10 Multiple comparisons after ANOVA
- 11 Two factor analysis of variance
- 12 Important assumptions of analysis of variance: transformations and a test for equality of variances
- 13 Two factor analysis of variance without replication, and nested analysis of variance
- 14 Relationships between variables: linear correlation and linear regression
- 15 Simple linear regression
- 16 Non-parametric statistics
- 17 Non-parametric tests for nominal scale data
- 18 Non-parametric tests for ratio, interval, or ordinal scale data
- 19 Choosing a test
- 20 Doing science responsibly and ethically
- References
- Index
Summary
Introduction
Life scientists often collect samples in which the experimental units can be assigned to two or more discrete and mutually exclusive categories. For example, a sample of 20 humans can be partitioned into the two mutually exclusive categories of ‘right-handed’ or ‘left-handed’ (since even people who claim to be ambidextrous still perform a greater proportion of actions with one hand and can be classified as having a dominant right or left hand). These two categories are discrete because there is no intermediate state and mutually exclusive because a person cannot be assigned to both. They also make up the entire set of possible outcomes within the sample and therefore are contingent upon each other, since for a fixed sample size a decrease in the number in one category must be accompanied by an increase in the number in the other and vice versa.
These are nominal scale data (Chapter 3). The questions researchers ask about these data are the sort asked about any sample(s) from a population.
First, you may want to know the probability a sample has been taken from a population having a known or expected proportion within each category. For example, the proportion of left-handed people in the world is close to 0.1 (10%), which can be considered the proportion in the population, since it is from a sample of several million people.
- Type
- Chapter
- Information
- Statistics ExplainedAn Introductory Guide for Life Scientists, pp. 208 - 223Publisher: Cambridge University PressPrint publication year: 2005