Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Theory of Tests, p-Values, and Confidence Intervals
- 3 From Scientific Theory to Statistical Hypothesis Test
- 4 One-Sample Studies with Binary Responses
- 5 One-Sample Studies with Ordinal or Numeric Responses
- 6 Paired Data
- 7 Two-Sample Studies with Binary Responses
- 8 Assumptions and Hypothesis Tests
- 9 Two-Sample Studies with Ordinal or Numeric Responses
- 10 General Methods for Frequentist Inferences
- 11 k-Sample Studies and Trend Tests
- 12 Clustering and Stratification
- 13 Multiplicity in Testing
- 14 Testing from Models
- 15 Causality
- 16 Censoring
- 17 Missing Data
- 18 Group Sequential and Related Adaptive Methods
- 19 Testing Fit, Equivalence, and Noninferiority
- 20 Power and Sample Size
- 21 Bayesian Hypothesis Testing
- References
- Notation Index
- Concept Index
1 - Introduction
Published online by Cambridge University Press: 17 April 2022
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Theory of Tests, p-Values, and Confidence Intervals
- 3 From Scientific Theory to Statistical Hypothesis Test
- 4 One-Sample Studies with Binary Responses
- 5 One-Sample Studies with Ordinal or Numeric Responses
- 6 Paired Data
- 7 Two-Sample Studies with Binary Responses
- 8 Assumptions and Hypothesis Tests
- 9 Two-Sample Studies with Ordinal or Numeric Responses
- 10 General Methods for Frequentist Inferences
- 11 k-Sample Studies and Trend Tests
- 12 Clustering and Stratification
- 13 Multiplicity in Testing
- 14 Testing from Models
- 15 Causality
- 16 Censoring
- 17 Missing Data
- 18 Group Sequential and Related Adaptive Methods
- 19 Testing Fit, Equivalence, and Noninferiority
- 20 Power and Sample Size
- 21 Bayesian Hypothesis Testing
- References
- Notation Index
- Concept Index
Summary
The chapter begins with a cautionary story of a study on hydroxychloroquine treatment for COVID-19. We detail weaknesses in the study that led to misinterpretation of its results, emphasizing that there is more to proper application of hypothesis tests than calculating a p-value. We then discuss reproducibility in science and the p-value controversy. Some argue that since p-values are often misunderstood and misused that they should be replaced with other statistics. We counter that point of view, arguing that frequentist hypothesis tests, when properly applied, are well suited to address reproducibility issues. This motivates the book which provides guiding principles and tools for designing studies and properly applying hypothesis testing for many different scientific applications. We agree with many of the concerns about overreliance on p-values, hence the approach of the book is to present not just methods for hypothesis tests, but also methods for compatible confidence intervals on parameters that can accompany them. The chapter ends with an overview of the book, describing its level and the intended audience.
- Type
- Chapter
- Information
- Statistical Hypothesis Testing in ContextReproducibility, Inference, and Science, pp. 1 - 7Publisher: Cambridge University PressPrint publication year: 2022
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