Chapter 1: A Gentle IntroductionHow Much Math Do I Need to Do Statistics?The General Purpose of Statistics: Understanding the WorldWhat Is a Statistician?Liberal and Conservative StatisticiansDescriptive and Inferential StatisticsExperiments Are Designed to Test Theories and HypothesesOddball TheoriesBad Science and MythsEight Essential Questions of Any Survey or StudyOn Making Samples Representative of the PopulationExperimental Design and Statistical Analysis as ControlsThe Language of StatisticsOn Conducting Scientific ExperimentsThe Dependent Variable and MeasurementOperational DefinitionsMeasurement ErrorMeasurement Scales: The Difference Between Continuous and Discrete VariablesTypes of Measurement ScalesRounding Numbers and Rounding ErrorStatistical SymbolsSummaryHistory Trivia: Achenwall to NightingaleKey Terms, Symbols, and DefinitionsChapter 1 Practice ProblemsChapter 1 Test Yourself QuestionsSPSS Lesson 1Chapter 2: Descriptive Statistics: Understanding Distributions of NumbersThe Purpose of Graphs and Tables: Making Arguments and DecisionsA Summary of the Purpose of Graphs and TablesGraphical CautionsFrequency DistributionsShapes of Frequency DistributionsGrouping Data Into IntervalsAdvice on Grouping Data Into IntervalsThe Cumulative Frequency DistributionCumulative Percentages, Percentiles, and QuartilesStem-and-Leaf PlotNonnormal Frequency DistributionsOn the Importance of the Shapes of DistributionsAdditional Thoughts About Good Graphs Versus Bad GraphsHistory Trivia: De Moivre to TukeyKey Terms and DefinitionsChapter 2 Practice ProblemsChapter 2 Test Yourself QuestionsSPSS Lesson 2Chapter 3: Statistical Parameters: Measures of Central Tendency and VariationMeasures of Central TendencyChoosing Between Measures of Central TendencyKlinkers and OutliersUncertain or Equivocal ResultsMeasures of VariationCorrecting for Bias in the Sample Standard DeviationHow the Square Root of x2 Is Almost Equivalent to Taking the Absolute Value of xThe Computational Formula for Standard DeviationThe VarianceThe Sampling Distribution of Means, the Central Limit Theorem, and the Standard Error of the MeanThe Use of the Standard Deviation for PredictionPractical Uses of the Empirical Rule: As a Definition of an OutlierPractical Uses of the Empirical Rule: Prediction and IQ TestsSome Further CommentsHistory Trivia: Fisher to EelsKey Terms, Symbols, and DefinitionsChapter 3 Practice ProblemsChapter 3 Test Yourself QuestionsSPSS Lesson 3Chapter 4: Standard Scores, the z Distribution, and Hypothesis TestingStandard ScoresThe Classic Standard Score: The z Score and the z DistributionCalculating z ScoresMore Practice on Converting Raw Data Into Z ScoresThe z DistributionInterpreting Negative z ScoresTesting the Predictions of the Empirical Rule With the z DistributionWhy Is the z Distribution So Important?How We Use the z Distribution to Test Experimental HypothesesMore Practice With the z Distribution and T ScoresSummarizing Scores Through PercentilesHistory Trivia: Karl Pearson to Egon PearsonKey Terms and DefinitionsChapter 4 Practice ProblemsChapter 4 Test Yourself QuestionsSPSS Lesson 4Chapter 5: Inferential Statistics: The Controlled Experiment, Hypothesis Testing, and the z DistributionHypothesis Testing in the Controlled ExperimentHypothesis Testing: The Big DecisionHow the Big Decision Is Made: Back to the z DistributionThe Parameter of Major Interest in Hypothesis Testing: The MeanNondirectional and Directional Alternative HypothesesA Debate: Retain the Null Hypothesis or Fail to Reject the Null HypothesisThe Null Hypothesis as a Nonconservative BeginningThe Four Possible Outcomes in Hypothesis TestingSignificance LevelsSignificant and Nonsignificant FindingsTrends, and Does God Really Love the.05 Level of Significance More Than the.06 Level?Directional or Nondirectional Alternative Hypotheses: Advantages and DisadvantagesDid Nuclear Fusion Occur?Baloney DetectionConclusions About Science and PseudoscienceThe Most Critical Elements in the Detection of Baloney in Suspicious Studies and Fraudulent ClaimsCan Statistics Solve Every Problem?ProbabilityHistory Trivia: Egon Pearson to Karl PearsonKey Terms, Symbols, and DefinitionsChapter 5 Practice ProblemsChapter 5 Test Yourself QuestionsSPSS Lesson 5Chapter 6: An Introduction to Correlation and RegressionCorrelation: Use and AbuseA Warning: Correlation Does Not Imply CausationAnother Warning: Chance Is LumpyCorrelation and PredictionThe Four Common Types of CorrelationThe Pearson Product-Moment Correlation CoefficientTesting for the Significance of a Correlation CoefficientObtaining the Critical Values of the t DistributionIf the Null Hypothesis Is RejectedRepresenting the Pearson Correlation Graphically: The ScatterplotFitting the Points With a Straight Line: The Assumption of a Linear RelationshipInterpretation of the Slope of the Best-Fitting LineThe Assumption of HomoscedasticityThe Coefficient of Determination: How Much One Variable Accounts for Variation in Another Variable: The Interpretation of r2Quirks in the Interpretation of Significant and Nonsignificant Correlation CoefficientsLinear RegressionReading the Regression LineThe World is a Complex Place: Any Single Behavior is Most Often Caused by Multiple VariablesFinal Thoughts About Regression Analyses: A Warning about the Interpretation of the Significant Beta CoefficientsSpearman''s CorrelationSignificance Test for Spearman''s rTies in RanksPoint-Biserial CorrelationTesting for the Significance of the Point-Biserial Correlation CoefficientPhi (f) CorrelationTesting for the Significance of PhiHistory Trivia: Galton to FisherKey Terms, Symbols, and DefinitionsChapter 6 Practice ProblemsChapter 6 Test Yourself QuestionsSPSS Lesson 6Chapter 7: The t Test for Independent GroupsThe Statistical Analysis of the Controlled ExperimentOne t Test but Two DesignsAssumptions of the Independent t TestThe Formula for the Independent t TestYou Must Remember This! An Overview of Hypothesis Testing With the t TestWhat Does the t Test Do? Components of the t Test FormulaWhat If the Two Variances Are Radically Different From One Another?A Computational ExampleMarginal SignificanceThe Power of a Statistical TestEffect SizeThe Correlation Coefficient of Effect SizeAnother Measure of Effect Size: Cohen''s dConfidence IntervalsEstimating the Standard ErrorHistory Trivia: Gosset and Guinness BreweryKey Terms and DefinitionsChapter 7 Practice ProblemsChapter 7 Test Yourself QuestionsSPSS Lesson 7Chapter 8: The t Test for Dependent GroupsAssumptions of the Dependent t TestWhy the Dependent t Test May Be More Powerful Than the Independent t TestHow to Increase the Power of a t TestDrawbacks of the Dependent t Test DesignsOne-Tailed or Two-Tailed Tests of SignificanceHypothesis Testing and the Dependent t Test: Design 1Design 1 (Same Participants or Repeated Measures): A Computational ExampleDesign 2 (Matched Pairs): A Computational ExampleDesign 3 (Same Participants and Balanced Presentation): A Computational ExampleHistory Trivia: Fisher to PearsonKey Terms and DefinitionsChapter 8 Practice ProblemsChapter 8 Test Yourself QuestionsSPSS Lesson 8Chapter 9: Analysis of Variance (ANOVA): One-Factor Completely Randomized DesignA Limitation of Multiple t Tests and a SolutionThe Equally Unacceptable Bonferroni SolutionThe Acceptable Solution: An Analysis of VarianceThe Null and Alternative Hypotheses in ANOVAThe Beauty and Elegance of the F Test StatisticThe F RatioHow Can There Be Two Different Estimates of Within-Groups Variance?ANOVA DesignsANOVA AssumptionsPragmatic OverviewWhat a Significant ANOVA IndicatesA Computational ExampleDegrees of Freedom for the NumeratorDegrees of Freedom for the DenominatorDetermining Effect Size in ANOVA: Omega-Squared (w2)Another Measure of Effect Size: Eta (h)History Trivia: Gosset to FisherKey Terms and DefinitionsChapter 9 Practice ProblemsChapter 9 Test QuestionsChapter 9 Test Yourself QuestionsChapter 10: After a Significant Analysis of Variance: Multiple Comparison TestsConceptual Overview of Tukey''s TestComputation of Tukey''s HSD TestWhat to Do If the Error Degrees of Freedom Are Not Listed in the Table of Tukey''s q ValuesWarning!Determining What It All MeansOn the Importance of Nonsignificant Mean DifferencesFinal Results of ANOVAQuirks in InterpretationTukey''s With Unequal NsKey Terms, Symbols, and DefinitionsChapter 10 Practice ProblemsChapter 10 Test Yourself QuestionsSPSS Lesson 10Chapter 11: Analysis of Variance (ANOVA): One-Factor Repeated-Measures DesignThe Repeated-Measures ANOVAAssumptions of the One-Factor Repeated-Measures ANOVAComputational ExampleDetermining Effect Size in ANOVAKey Terms and DefinitionsChapter 11 Practice ProblemsChapter 11 Test Yourself QuestionsSPSS Lesson 11Chapter 12: Factorial ANOVA: Two-Factor Completely Randomized DesignFactorial DesignsThe Most Important Feature of a Factorial Design: The InteractionFixed and Random Effects and In Situ DesignsThe Null Hypotheses in a Two-Factor ANOVAAssumptions and Unequal Numbers of ParticipantsComputational ExampleKey Terms and DefinitionsChapter 12 Practice ProblemsChapter 12 Test Yourself ProblemsSPSS Lesson 12Chapter 13: Post Hoc Analysis of Factorial ANOVAMain Effect Interpretation: GenderWhy a Multiple Comparison Test Is Unnecessary for a Two-Level Main Effect, and When Is a Multiple Comparison Test Necessary?Main Effect: Age LevelsMultiple Comparison Test for the Main Effect for AgeWarning: Limit Your Main Effect Conclusions When the Interaction Is SignificantMultiple Comparison TestsInterpretation of the Interaction EffectFinal SummaryWriting Up the Results Journal StyleLanguage to AvoidExploring the Possible Outcomes in a Two-Factor ANOVADetermining Effect Size in a Two-Factor ANOVAHistory Trivia: Fisher and SmokingKey Terms, Symbols, and DefinitionsChapter 13 Practice ProblemsChapter 13 Test Yourself QuestionsSPSS Lesson 13Chapter 14: Factorial ANOVA: Additional DesignsThe Split-Plot DesignOverview of the Spl.