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Preface | |

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About the Authors | |

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Introduction | |

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Data Collection and Exploration | |

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Getting Started With Statistical Analysis: Where Do I Obtain Data, and How Do I Prepare Data for Statistical Analysis? | |

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Where Data Come From | |

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Primary Data Analysis | |

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Secondary Data Analysis | |

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Preparing Data for Analysis | |

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Cases: How Do I Define a Unit of Analysis? | |

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How Do I Use Variables and Values to Describe a Case? | |

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Are There Rules I Should Use for Naming Variables? | |

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What Is Data Coding? | |

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What About Open-Ended Response Choices? How Are These Coded? | |

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How Do I Code Open-Ended Questions? | |

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What Are the Basic Guidelines of Data Organization? | |

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How Do I Define and Code Missing Data? | |

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How Do I Code Nonspecific Responses? | |

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How Do I Deal With Blanks and Zeros? | |

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A Detailed Example of Data Organization and Coding | |

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The Questionnaire | |

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The Codebook | |

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How Do I Examine Data Prior to Analysis? | |

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When Should I Screen Data? | |

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How Do I Display My Data Visually? | |

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What Is a Box and Whisker Plot? | |

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What Is a Stem-and-Leaf Diagram? | |

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What Is a Normal Distribution? | |

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How Do I Prepare to Analyze Categorical Data? | |

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How Do I Examine Two Variables at the Same Time? | |

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How Do I Use a Crosstabulation to Examine Categorical Variables? | |

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How Do I Use a Scatterplot to Examine Continuously Distributed Bivariate Data? | |

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How Do I Display Bivariate Data if the Independent Variable Is Categorical and the Dependent Variable Continuous? | |

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Simple Bar Chart for Means | |

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Summary | |

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The Logic of Statistical Analysis: Issues Regarding the Nature of Statistics and Statistical Tests | |

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Traditional Approaches to Statistical Analysis and the Logic of Statistical Inference | |

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What Is the Difference Between Descriptive and Inferential Statistics? | |

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What Is a Statistical Generalization? | |

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What Do We Mean by "Sampling Error"? | |

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What Are Sampling Distributions? | |

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Some Examples: Sampling Distributions Based on Samples of Different Sizes | |

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What Is a Confidence Interval? | |

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What Is a Hypothesis Test? | |

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Rethinking Traditional Paradigms: Power, Effect Size, and Hypothesis-Testing Alternatives | |

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What Is the Difference Between Statistical and Substantive Significance? | |

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What Is Statistical Power? | |

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Binomial Effect Size Displays | |

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An Example Using Odds Ratios | |

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What Are the Relationships Among Sample Size, Sampling Error, Effect Size, and Power? | |

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Confidence Intervals Revisited | |

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What Are the Problems With Statistical Significance Testing? | |

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Has Significance Testing Outlived Its Usefulness? | |

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What Axe the Assumptions of Statistical Testing? | |

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What Are the Assumptions Related to Characteristics of Population Distributions? | |

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What Are Normality and Multivariate Normality? | |

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Is the Distribution Normal? | |

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How Do I Examine Data for Bivariate and Multivariate Normality? | |

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What Is Homoscedasticity? | |

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What Are the Assumptions About Error or Disturbance Terms? | |

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What Are the Assumptions About the Sample? | |

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How Important Are Random Samples? | |

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How Do I Calculate an Appropriate Sample Size? | |

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A Reconsideration of the Sample Size Issue | |

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What Is Independence of Observations? | |

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What Are the Assumptions About Measurement? | |

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What Is Measurement Error? | |

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Explicit Recognition of the "Measurement Model" | |

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What Are the Assumptions About the Statistical Model? | |

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When Should I Be Concerned About Meeting the Assumptions of a Test? | |

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An Introduction to Statistical Models: Explaining Relationship Patterns | |

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Our Notation System | |

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Modeling Three-Variable Relationships | |

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The Full Mediation Model (The Intervening Effects Explanation) | |

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The Spurious Explanation | |

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The Joint Effects Explanation | |

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The Interaction Effect or Moderation Explanation | |

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The Suppressor Variable Explanation | |

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Summary: Patterns of Relationships and Their Explanations | |

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When Do I Control Variables? | |

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An Example of Model Building | |

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Model Representing the First Hypothesis | |

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Modification of the Model to Include Hypothesis 3 | |

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Modification of the Model to Include Hypothesis 4 | |

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Modification of the Model to Include Hypothesis 5 | |

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How Do I Select the Appropriate Statistical Test? | |

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Am I Comparing Groups or Examining Relationships? | |

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When Should I Use Multivariate Analysis? | |

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How Do I Select the Appropriate Statistical Test? | |

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Major Research Questions Suggested by the Four Design Frameworks | |

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Statistical Analyses Suggested by Each Design Framework | |

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Design 1 | |

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Design 2 | |

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Design 3 | |

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Design 4 | |

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Describing the Techniques | |

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Issues Related to Variables and their Distribution | |

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How Do I Deal With Missing Values, Outliers, and Non-normality? | |

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How Do I Deal With Missing Values? | |

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Three Patterns of Missing Data: MAR, MCAR, and MNAR | |

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Adjusting for MCAR Data | |

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Adjusting for Missing, Non-MCAR Data | |

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Case Deletion | |

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Imputation | |

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Regression | |

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Adjusting for Missing Outcomes Due to Participant Attrition | |

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Maximum Likelihood Methods | |

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Multiple Imputation Methods | |

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Adjusting for Missing Values: Summary | |

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How Do I Control or Adjust for Outliers? | |

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Identifying Outliers | |

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Univariate Distributions | |

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Bivariate and Multivariate Distributions | |

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Adjusting Data for Outliers | |

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How Do I Adjust for Non-normal Data? | |

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Data Transformation | |

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Power Transformations | |

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Limitations of Data Transformation | |

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Types of Variables and Their Treatment in Statistical Analysis | |

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How Do I Determine Whether a Parametric or Nonparametric Test Is Best? | |

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Parametric Tests and Their Nonparametric Alternatives | |

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What Are Dummy Variables, and How Do I Code Them? | |

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When, If Ever, Should I Dichotomize a Continuous Variable? | |

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Recommendations | |

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Understanding the Big Two: Major Questions About Analysis of Variance and Regression Analysis | |

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Questions About Analysis of Variance | |

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What Are the Nuts and Bolts of Analysis of Variance? | |

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What Is an Interaction Effect, and How Do I Interpret It? | |

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What Are the Issues in the Interpretation of Interaction? | |

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Recommendations | |

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How Do I Select the Best Method for Analyzing the Pretest-Posttest Design? | |

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What Is an Analysis of Covariance? | |

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Recommendations | |

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What Are Planned and Post Hoc Comparisons? | |

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What Are Planned and A Priori Contrasts? | |

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What Are Post Hoc and A Posteriori Tests? | |

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Recommendations | |

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How Do I Control for Familywise Error? | |

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What Are Familywise Error Rates? | |

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Recommendations | |

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When Should I Use MANOVA? | |

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What Is the Distinction Between a MANOVA and an ANOVA? | |

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Recommendations | |

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Questions About Multiple Regression Analysis | |

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What Are the Nuts and Bolts of Multiple Regression Analysis? | |

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How Do I Determine the Appropriate Number of Subjects and Predictor Variables? | |

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What Are Stepwise and Hierarchical Multiple Regression Procedures, and When Should I Use Each? | |

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Describing Relationships Between Independent and Dependent Variables | |

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Predicting the Value of the Dependent Variable | |

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Testing a Theoretical Model | |

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A Detailed Example: The Effect of Age and Education on Income | |

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What Are Regression Coefficients, and How Do I Interpret Them? | |

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How Do I Interpret Bivariate Correlation Coefficients? | |

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How Do I Interpret Multiple Correlations? | |

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What Are Unstandardized and Standardized Regression Coefficients? | |

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How Do I Interpret Partial and Semipartial Correlations? | |

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Partitioning of Variability for Bivariate Squared Correlations (r<sup>2</sup>) | |

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Partitioning of Variability for Semipartial Squared Correlations (r<sup>2</sup>) | |

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Partitioning of Variability for Partial Squared Correlations | |

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How Do I Interpret an Interaction Effect? | |

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The Bigger Picture | |

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How Do I Understand the Relationships Among the Different Statistical Techniques? | |

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When Should I Use Meta-Analysis? | |

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The Pros and Cons of Meta-Analysis | |

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An Example of Meta-Analysis | |

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What Are Modern Robust Statistics? | |

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Ten Tips for Success in Statistical Analysis | |

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Get Comfortable With Your Data | |

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Thoroughly Explore Your Data, Twice! | |

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Sometimes Pictures Speak Louder Than Words | |

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Replication Is Underemphasized and Overdue | |

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Remember the Distinction Between Statistical Significance and Substantive Significance | |

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Remember the Distinction Between Statistical Significance and Effect Size | |

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Statistics Do Not Speak for Themselves | |

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Keep It Simple When Possible | |

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Use Consultants | |

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Don't Be Too Hard on Yourself | |

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References | |

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Index | |