Herzog M. Understanding Statistics And Experimental Design...2019

Textbook in PDF format This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets. Science, Society, and Statistics About This Book The Essentials of Statistics The Basic Scenario A Second Test One More Example: Guillain-Barré Syndrome Basics About Odds Ratios (OR) Partial Information and the World of Disease The Classic Scenario of SDT SDT and the Percentage of Correct Responses The Empirical d Another Way to Estimate the Signal-to-Noise Ratio Undersampling Sampling Distribution of a Mean Comparing Means The Type I and II Error Type I Error: The p-Value is Related to a Criterion Type II Error: Hits, Misses An Example Implications, Comments and Paradoxes A Bit of Terminology One-Sample t-Test Dependent Samples t-Test The Data Need to be Independent and Identically Distributed Ratio Scale Dependent Variable Fixed Sample Size The Essentials of Statistical Tests What Comes Next? The Multiple Testing Problem Independent Tests How Many Scientific Results Are Wrong? One-Way Independent Measures ANOVA Logic of the ANOVA What the ANOVA Does and Does Not Tell You: Post-Hoc Tests Computation of the ANOVA Post-Hoc Tests Effect Size Two-Way Independent Measures ANOVA Repeated Measures ANOVA Model Fits Optimizing the Design Computing Power Power Challenges for Complex Designs Covariance and Correlations Hypothesis Testing with Correlations Interpreting Correlations Comparison to Model Fitting, ANOVA and t-Test Regression Meta-analysis and the Science Crisis Standardized Effect Sizes Meta-analysis Standardized Effect Sizes Beyond the Simple Case Extended Example of the Meta-analysis The Replication Crisis Test for Excess Success (TES) Excess Success from Publication Bias Excess Success from Optional Stopping Excess Success and Theoretical Claims You Probably Have Trouble Detecting Bias How Extensive Are These Problems? Misunderstanding Replication Hypothesizing After the Results Are Known (HARKing) Misunderstanding Prediction Sloppiness and Selective Double Checking Preregistration Alternative Statistical Analyses The Role of Replication A Focus on Mechanisms

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