Common errors in statistics (and how to avoid them), (paperback) (2^nd Ed.)

Common Errors is consistently coherent and provides a consistent level throughout. The Second Edition elaborates on a few key topics such as testing hypotheses (two-way designs) and multivariate regression (nonuniqueness and indicator variables).

FOUNDATIONS. Sources of Error. Prescription. Fundamental Concepts. Ad Hoc, Post Hoc Hypotheses. 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. Collecting Data. Preparation. Measuring Devices. Determining Sample Size. Fundamental Assumptions. Experimental Design. Four Guidelines. Are Experiments Really Necessary? HYPOTHESIS TESTING AND ESTIMATION. Estimation. Prevention. Desirable and Not-So-Desirable Estimators. Interval Estimates. Improved Results. 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. Strengths and Limitations of Some Miscellaneous Statistical Procedures. Bootstrap. Bayesian Methodology. Meta-Analysis. Permutation Tests. Reporting Your Results. Fundamentals. Tables. Standard Error. p-Values. Confidence Intervals. Recognizing and Reporting Biases. Reporting Power. Drawing Conclusions. Interpreting Reports. With A Grain of Salt. Rates and Percentages. Interpreting Computer Printouts. Graphics. The Soccer Data. Five Rules for Avoiding Bad Graphics. One Rule for Correct Usage of Three-Dimensional Graphics. The Misunderstood Pie Chart. Two Rules for Effective Display of Subgroup Information. Two Rules for Text Elements in Graphics. Multidimensional Displays. Choosing Graphical Displays. BUILDING A MODEL. Univariate Regression. Model Selection. Estimating Coefficients. Further Considerations. Alternate Methods of Regression. Linear vs. Nonlinear Regression. Least Absolute Deviation Regression. Errors-in-Variables Regression. Quantile Regression. The Ecological Fallacy. Nonsense Regression. Multivariable Regression. Caveats. Factor Analysis. General Linearized Models. Reporting Your Results. A Conjecture. Decision Trees. Building a Successful Model. Validation. Methods of Validation. Measures of Predictive Success. Long-Term Stability.