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Understanding Statistics and Experimental Design : How to Not Lie with Statistics.

Bibliographic Details
Main Author: Herzog, Michael H.
Other Authors: Francis, Gregory., Clarke, Aaron.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2019.
Edition:1st ed.
Series:Learning Materials in Biosciences Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Science, Society, and Statistics
  • About This Book
  • Contents
  • Part I The Essentials of Statistics
  • 1 Basic Probability Theory
  • Contents
  • 1.1 Confusions About Basic Probabilities: Conditional Probabilities
  • 1.1.1 The Basic Scenario
  • 1.1.2 A Second Test
  • 1.1.3 One More Example: Guillain-BarreĢ Syndrome
  • 1.2 Confusions About Basic Probabilities: The Odds Ratio
  • 1.2.1 Basics About Odds Ratios (OR)
  • 1.2.2 Partial Information and the World of Disease
  • References
  • 2 Experimental Design and the Basics of Statistics: Signal Detection Theory (SDT)
  • Contents
  • 2.1 The Classic Scenario of SDT
  • 2.2 SDT and the Percentage of Correct Responses
  • 2.3 The Empirical d
  • 3 The Core Concept of Statistics
  • Contents
  • 3.1 Another Way to Estimate the Signal-to-Noise Ratio
  • 3.2 Undersampling
  • 3.2.1 Sampling Distribution of a Mean
  • 3.2.2 Comparing Means
  • 3.2.3 The Type I and II Error
  • 3.2.4 Type I Error: The p-Value is Related to a Criterion
  • 3.2.5 Type II Error: Hits, Misses
  • 3.3 Summary
  • 3.4 An Example
  • 3.5 Implications, Comments and Paradoxes
  • Reference
  • 4 Variations on the t-Test
  • Contents
  • 4.1 A Bit of Terminology
  • 4.2 The Standard Approach: Null Hypothesis Testing
  • 4.3 Other t-Tests
  • 4.3.1 One-Sample t-Test
  • 4.3.2 Dependent Samples t-Test
  • 4.3.3 One-Tailed and Two-Tailed Tests
  • 4.4 Assumptions and Violations of the t-Test
  • 4.4.1 The Data Need to be Independent and Identically Distributed
  • 4.4.2 Population Distributions are Gaussian Distributed
  • 4.4.3 Ratio Scale Dependent Variable
  • 4.4.4 Equal Population Variances
  • 4.4.5 Fixed Sample Size
  • 4.5 The Non-parametric Approach
  • 4.6 The Essentials of Statistical Tests
  • 4.7 What Comes Next?
  • Part II The Multiple Testing Problem
  • 5 The Multiple Testing Problem
  • Contents
  • 5.1 Independent Tests.
  • 5.2 Dependent Tests
  • 5.3 How Many Scientific Results Are Wrong?
  • 6 ANOVA
  • Contents
  • 6.1 One-Way Independent Measures ANOVA
  • 6.2 Logic of the ANOVA
  • 6.3 What the ANOVA Does and Does Not Tell You: Post-Hoc Tests
  • 6.4 Assumptions
  • 6.5 Example Calculations for a One-Way Independent Measures ANOVA
  • 6.5.1 Computation of the ANOVA
  • 6.5.2 Post-Hoc Tests
  • 6.6 Effect Size
  • 6.7 Two-Way Independent Measures ANOVA
  • 6.8 Repeated Measures ANOVA
  • 7 Experimental Design: Model Fits, Power, and Complex Designs
  • Contents
  • 7.1 Model Fits
  • 7.2 Power and Sample Size
  • 7.2.1 Optimizing the Design
  • 7.2.2 Computing Power
  • 7.3 Power Challenges for Complex Designs
  • 8 Correlation
  • Contents
  • 8.1 Covariance and Correlations
  • 8.2 Hypothesis Testing with Correlations
  • 8.3 Interpreting Correlations
  • 8.4 Effect Sizes
  • 8.5 Comparison to Model Fitting, ANOVA and t-Test
  • 8.6 Assumptions and Caveats
  • 8.7 Regression
  • Part III Meta-analysis and the Science Crisis
  • 9 Meta-analysis
  • Contents
  • 9.1 Standardized Effect Sizes
  • 9.2 Meta-analysis
  • Appendix
  • Standardized Effect Sizes Beyond the Simple Case
  • Extended Example of the Meta-analysis
  • 10 Understanding Replication
  • Contents
  • 10.1 The Replication Crisis
  • 10.2 Test for Excess Success (TES)
  • 10.3 Excess Success from Publication Bias
  • 10.4 Excess Success from Optional Stopping
  • 10.5 Excess Success and Theoretical Claims
  • Reference
  • 11 Magnitude of Excess Success
  • Contents
  • 11.1 You Probably Have Trouble Detecting Bias
  • 11.2 How Extensive Are These Problems?
  • 11.3 What Is Going On?
  • 11.3.1 Misunderstanding Replication
  • 11.3.2 Publication Bias
  • 11.3.3 Optional Stopping
  • 11.3.4 Hypothesizing After the Results Are Known (HARKing)
  • 11.3.5 Flexibility in Analyses
  • 11.3.6 Misunderstanding Prediction.
  • 11.3.7 Sloppiness and Selective Double Checking
  • 12 Suggested Improvements and Challenges
  • Contents
  • 12.1 Should Every Experiment Be Published?
  • 12.2 Preregistration
  • 12.3 Alternative Statistical Analyses
  • 12.4 The Role of Replication
  • 12.5 A Focus on Mechanisms.

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