Examine and solve the common misconceptions and fallacies that non-statisticians bring to their interpretation of statistical results. Explore the many pitfalls that non-statisticians--and also statisticians who present statistical reports to non-statisticians--must avoid if statistical results are to be correctly used for evidence-based business decision making.

Chapter 1: Design of Experiments; Forming the Study Question; Forming Hypotheses; Information Required; Power and Sample Size; Calculation Information; Risk (Confidence and Power Levels); Continuous Data; Binary Data; Conducting the Calculations; Defining the Scope of the Study; Applicability of Results; Assumptions; Experimental Design; Variables; Interactions; Confounding; Designed Experiments; Physical Experiments; Factorial and Optimal Designs; Adaptive Designs; Other Designs; Computer Experiments; Surveys; Summary.

Chapter 2: Data Collection Collecting the Data; Objective Data; Variation; Repeats; Precision; Subjective Data; Participants; Instructions; Repeats; Observers; Instructions; Authority; Variation; Formatting; Summary;

Chapter 3: Exploratory Data Analysis; Data Types; Quantitative Data; Continuous Data; Discrete Data; Qualitative Data; Binary Data; Nominal Data; Ordinal Data; Viewing the Data; Bar Charts; Dot Plots; Parallel Lines Plots; Histograms; Scatter Plots; Line Graphs; Box Plots; Likert Plots; Trellis Graphs; Outliers; Distribution; Tests; Continuous Data; Discrete Data; Summary.

Chapter 4: Descriptive StatisticsContinuous Data; Shape; Skewness; Kurtosis; Transformations; Location; Mode; Median; Mean; Weighted Mean; Spread; Standard Deviation and Variance; Range; Quantiles and Percentiles; IQR and SIQR; MAD; CV; Discrete Data; Bivariate Data; Contingency Tables; Correlation and Covariance; Summary;

Chapter 5: Measuring Uncertainty; Confidence Intervals; Continuous Data; Binary Data; Tolerance Intervals; Continuous Data; Binary Data; Prediction Intervals; Summary;

Chapter 6: Hypothesis Testing; Hypothesis Test Components; Hypotheses; Sides or Tails; P-values. Significant Differences Practical Differences; Plots; Interpretation; Hypothesis Tests; Binary Data; One-Sample Binary Data; Two-Sample Binary Data; Paired Binary Data; Continuous Data; One-Sample Normally Distributed Data; One-Sample Non-Normally Distributed Data; Two-Sample Normally Distributed Data; Two-Sample Non-Normally Distributed Data; Paired Normally Distributed Data; Paired Non-Normally Distributed Data; Summary;

Chapter 7: Statistical Modeling; Statistical Model Components; Model Assumptions; Model Structure; Model Process; Model Output; Statistical Models; Simple Models. Linear ModelANOVA; Generalized Linear Model; Gaussian GLM; Poisson GLM; Negative Binomial GLM; Binomial GLM; Bias-Reduction Binomial-Response RGLM; Zero-Inflated Models; Ordinal Logistic Regression; Linear Mixed-Effects Models; Summary;

Chapter 8: Multivariate Analysis; Multivariate Analysis of Variance; Principal Component Analysis; Q Methodology; Summary;

Chapter 9: Graphs; Common Plotting Mistakes; 3D and Pie Charts; Plotting Averages; Multiple Plots; Plotting Ordinal Data; Open Text Responses; Unnecessary Plots; Display; Graph Aesthetics in R; Graphs in R; Bar Chart; Tile Plot

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