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| Introduction | |
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| What Is Sampling Design? | |
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| What We Hope You Get from This Book | |
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| Outline of the book | |
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| Flow Charts and Scientific Questions | |
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| Constructing an Initial Model | |
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| Three types of questions | |
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| How Big Is Your Problem? | |
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| Where to go from here? | |
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| Describing Things: Some "Scientific" Conventions and Some Useful Techniques | |
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| How to Give a False Impression with a Computer | |
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| How Much Evidence Is Enough? | |
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| How Good Is Your Information? | |
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| When Highly Improbable Means Very Likely | |
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| How Textbooks Tell the Story | |
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| How Statisticians Count Independent Observations | |
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| Understanding the Statistical World with Ease | |
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| How to Avoid Accumulating Risk in Simple Comparisons | |
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| What Sort of Risk Are We Worried About? | |
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| Using Variability to Recognize a Difference | |
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| An Important Assumption | |
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| Partitioning Variance | |
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| Analyses for a World with All Shades of Gray | |
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| What Sort of Risk Are We Worried About? | |
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| Putting the World into Boxes | |
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| Describing a Straight World | |
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| How Good a Fit Is the Model? | |
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| Real-World Problems: More Than One Factor | |
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| Adding Things Together | |
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| Adding Partitioned Variability | |
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| Checking Assumptions with Partial Plots | |
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| Interactions | |
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| Which Variables Should I Analyze Statistically? | |
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| Artificial Intelligence | |
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| Computer Generated Phantom Variables | |
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| More Complex Models: How to String Things Together | |
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| Estimating Direct Effects | |
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| Estimating Indirect Effects | |
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| Some Problems with Path Analysis | |
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| Straightenening the World: Transformations and Other Tricks | |
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| Trial and Error Estimates without Transformation | |
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| Other Deviant Methods | |
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| General Linear Models | |
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| Maximum Likelihood | |
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| Pitfalls in Nonlinear Estimation | |
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| Multivariate Statistics: Cutting Down the Trees to Better See the Forest | |
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| Graphs of Gradients | |
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| Hypothetical Gradients | |
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| More Than One Dimension | |
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| Eigenvector Analyses | |
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| Deep Culture: Significance Tests | |
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| Association Matrix (Mantel) Analyses | |
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| Canonical Analyses | |
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| Discriminating between Groups | |
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| Categories that Grow on Trees | |
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| Selecting Variables | |
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| Multivariate Indices that Masquerade as Univariate Variables | |
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| Know What You Are Looking for before You Start | |
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| How to Write Better Backwards | |
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| A Simple Conceptual Scheme | |
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| Annoying Yet Important Details | |
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| "Why" Questions | |
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| A Final Comment | |
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| Tips for Teachers | |