1. Chapter 15 Strategies When Population Distributions are Not Normal:
Data Transformations and
Rank-Order Tests

2. Assumptions in the Standard Hypothesis-Testing Procedures
Populations follow a normal curve
Populations have equal variances
Ceiling and floor effects

3. Data Transformations
Data transformation
Square-root transformation

4. Data Transformations Legitimacy of data transformations
Kinds of data transformations
Square root transformation
Log transformation
Inverse transformation
Arcsine transformation

5. Rank-Order Tests Rank-order transformation Rank-order tests
Nonparametric tests
Parametric tests

6. Rank-Order Tests Basic logic The null hypothesis
Normal curve approximations
Using parametric tests with rank-transformed data

7. Comparison of Methods Advantages and disadvantages
Relative risk of Type I and Type II errors

8. Controversies and Recent Development
Computer-intensive methods
Randomization tests
Proposed alternative to parametric and nonparametric methods
Widely applicable
Unfamiliar to researchers

9. Reporting in Research Articles
Data transformations
Described just prior to analyses using them
Rank-order methods
Described much like any other kind of hypothesis test