Fundamentals of Statistical Inference: What is the Meaning of Random Error?

Preface
Contents
Abbreviations
Chapter 1: Introduction
Chapter 2: The Meaning of Scientific and Statistical Inference
	2.1 The Starting Point: Errors and the Assessment of Validity
	2.2 External Validity
	2.3 Internal Validity
	2.4 Chapter Summary: Scientific Inference Is More Than Statistical Inference
	2.5 Recommended Reading
Chapter 3: The Basics of Statistical Inference: Simple Random Sampling
	3.1 The Starting Point: Descriptive Statistics of a Given Dataset
	3.2 Random Sampling, Sampling Error, and Sampling Distribution
	3.3 Estimation and Estimation Uncertainty in Simple Random Sampling
		3.3.1 Sample-Based Estimation of Effect Sizes and Standard Errors
		3.3.2 An Illustrative Application: Gender Pay Gap
		3.3.3 Sample-to-Sample Variability of Point and Standard Error Estimates
	3.4 Chapter Summary: Statistical Assumptions Are Empirical Commitments
	3.5 Recommended Reading
Chapter 4: Estimation Uncertainty in Complex Sampling Designs
	4.1 Overview of Different Sampling Designs
	4.2 Stratified Sampling
	4.3 Cluster Sampling
	4.4 Convenience Samples Contaminated by Selection Bias
		4.4.1 Non-randomness: The Big Challenge in the Social Sciences
		4.4.2 Approaches to Correct for Selection Bias in Convenience Samples
	4.5 Full Populations and Finite Population Correction
	4.6 Chapter Summary: Inference Requires Considering the Sampling Design
	4.7 Recommended Reading
Chapter 5: Knowledge Accumulation Through Meta-analysis and Replications
	5.1 The Basics of Meta-analysis
		5.1.1 Dealing with Different Measurements and Model Specifications
		5.1.2 Synthesizing Effect Sizes and Standard Errors Across Several Studies
	5.2 Evaluation of the Quality of Research Through Replications
	5.3 Chapter Summary: Our Best Estimators Estimate Correctly on Average
	5.4 Recommended Reading
Chapter 6: The p-Value and Statistical Significance Testing
	6.1 The p-Value Concept
	6.2 Null-Hypothesis-Significance-Testing
		6.2.1 Dichotomization of the p-Value and Significance Declarations
		6.2.2 The Statistical Ritual ``NHST´´ and Misinterpretations of Single Studies
		6.2.3 Perpetuation of the Statistical Ritual ``NHST´´ in Replication Studies
		6.2.4 Malpractices and Publication Bias Associated with NHST
		6.2.5 Approaches Aimed at Mitigating Publication Bias
	6.3 The Historical Origins of the NHST-Framework
		6.3.1 NHST: An Ill-bred Hybrid of Two Irreconcilable Statistical Approaches
		6.3.2 Inductive Behavior (Hypothesis Testing) and Type I Error Rates α
		6.3.3 Inductive Belief (Significance Testing) and p-Value Thresholds
	6.4 Chapter Summary: Significance Declarations Should Be Avoided
	6.5 Recommended Reading
Chapter 7: Statistical Inference in Experiments
	7.1 Inferential Cases in Group Mean Comparisons
	7.2 Causal Inference
		7.2.1 Overview of Experimental Designs Aimed at Establishing Causality
		7.2.2 The Uncertainty of Causal Effect Estimates Caused by Randomization
		7.2.3 Inference in Random Assignment of Randomly Recruited Subjects
	7.3 Inferences Without Randomization or Random Sampling
		7.3.1 Fictitious Random Sampling
		7.3.2 Fictitious Randomization
	7.4 Chapter Summary: Causal Inference Is Different from Generalization
	7.5 Recommended Reading
Chapter 8: Better Inference in the 21st Century: A World Beyond p < 0.05
References
Index