Common errors in statistics (and how to avoid them)

Pt. I. Foundations

1. Sources of error

2. Hypotheses : the why of your research

3. Collecting data

Pt. II. Statistical analysis

4. Data quality assessment

5. Estimation

6. Testing hypotheses : choosing a test statistic

7. Miscellaneous statistical practices

Pt. III. Reports

8. Reporting your results

9. Interpreting reports

10. Graphics

Pt. IV. Building a model

11. Univariate regression

12. Alternate methods of regression

13. Multivariable regression

14. Modeling correlated data

15. Validation.

Preface

pt. 1. Foundations

1. Sources of error

Prescription

Fundamental concepts

Ad hoc, post hoc hypotheses

2. Hypotheses : the why of your research

Prescription

What is a hypothesis?

How precise must a hypothesis be?

Found data

Null hypothesis

Neyman-Pearson theory

Deduction and induction

Losses

Decisions

To learn more

3. Collecting data

Preparation

Measuring devices

Determining sample size

Fundamental assumptions

Experimental design

Four guidelines

Are experiments really necessary?

To learn more

pt. 2. Hypothesis testing and estimation

4. Estimation

Prevention

Desirable and not-so-desirable estimators

Interval estimates

Improved results

Summary

To learn more

5. Testing hypotheses : choosing a test statistic

Comparing means of two populations

Comparing variances

Comparing the means of K samples

Higher-order experimental designs

Contingency tables

Inferior tests

Multiple tests

Before you draw conclusions

Summary

To learn more

6. Strengths and limitations of some miscellaneous statistical procedures

Bootstrap

Bayesian methodology

Meta-analysis

Permutation tests

To learn more

7. Reporting your results

Fundamentals

Tables

Standard error

p-values

Confidence intervals

Recognizing and reporting biases

Reporting power

Drawing conclusions

Summary

To learn more

8. Interpreting reports

With a grain of salt

Rates and percentages

Interpreting computer printouts

9. Graphics

The soccer data

Five rules for avoiding bad graphics

One rule for correct usage of three-dimensional graphics

One rule for the misunderstood pie chart

Two rules for effective display of subgroup information

Two rules for text elements in graphics

Multidimensional displays

Choosing effective display elements

Choosing graphical displays

Summary

To learn more

pt. 3. Building a model

10. Univariate regression

Model selection

Estimating coefficients

Further considerations

Summary

To learn more

11. Alternate methods of regression

Linear vs. Nonlinear regression

Least absolute deviation regression

Errors-in-variables regression

Quantile regression

The ecological fallacy

Nonsense regression

Summary

To learn more

12. Multivariable regression

Caveats

Factor analysis

Generalized linear models

Reporting your results

A conjecture

Building a successful model

To learn more

13. Validation

Methods of validation

Measures of predictive success

Long-term stability

To learn more

appendix A. A note on screening regression equations

appendix B. Cross-validation, the jackknife, and the bootstrap : excess error estimation in forward logistic regression

Glossary, grouped by related but distinct terms

Bibliography

Author index

Subject index.

I: Foundations

1. Sources of error

2. Hypotheses: the why of your research

3. Collecting data

II: Hypothesis testing and estimation

4. Estimation

5. Testing hypotheses: choosing a test statistic

6. Strengths and limitations of some miscellaneous statistical procedures

7. Reporting your results

8. Graphics

III: Building a model

9. Univariate regression

10. Multivariable regression

11. Validation.

Common Errors in Statistics (and How to Avoid Them); Contents; Preface; Part I: FOUNDATIONS; Chapter 1: Sources of Error; PRESCRIPTION; FUNDAMENTAL CONCEPTS; SURVEYS AND LONG-TERM STUDIES; AD-HOC, POST-HOC HYPOTHESES; TO LEARN MORE; Chapter 2: Hypotheses: The Why of Your Research; PRESCRIPTION; WHAT IS A HYPOTHESIS?; HOW PRECISE MUST A HYPOTHESIS BE?; FOUND DATA; NULL OR NIL HYPOTHESIS; NEYMAN-PEARSON THEORY; DEDUCTION AND INDUCTION; LOSSES; DECISIONS; TO LEARN MORE; Chapter 3: Collecting Data; PREPARATION; RESPONSE VARIABLES; DETERMINING SAMPLE SIZE; FUNDAMENTAL ASSUMPTIONS.