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This fully updated 4th edition explores the foundations of statistical reasoning, focusing on how to interpret psychological data and statistical results. The book is closely integrated with the free statistical analysis program JASP.The book also reflects the growing use of Bayesian analyses in the professional literature.
This fully updated 4th edition explores the foundations of statistical reasoning, focusing on how to interpret psychological data and statistical results. The book is closely integrated with the free statistical analysis program JASP.The book also reflects the growing use of Bayesian analyses in the professional literature.
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Autorenporträt
Arthur M. Glenberg is an emeritus Professor of Psychology at Arizona State University and at the University of Wisconsin-Madison. He is a Mercator Fellow at the University of Tübingen in Germany, and he holds an appointment at INICO at the University of Salamanca in Spain. He received his Ph.D. in 1974 from the University of Michigan.
Matthew E. Andrzejewski is a lecturer in the Department of Psychology at the University of Wisconsin-Whitewater. He received his Ph.D. in 2001 from Temple University.
Inhaltsangabe
01. Why Statistics? 02. Frequency Distributions and Percentiles 03. Central Tendency and Variability 04. z Scores and Normal Distributions 05. Overview of Inferential Statistics 06. Probability 07. Sampling Distributions 08. Logic of Hypothesis Testing 09. Power 10. Logic of Parameter Estimation 11. Inferences About Population Proportions Using the z Statistic 12. Inferences About mi When s Is Unknown: The Single-sample t Test 13. Comparing Two Populations: Independent Samples 14. Random Sampling, Random Assignment, and Causality 15. Comparing Two Populations: Dependent Samples 16. Comparing Two Population Means: The Independent Sample ANOVA 17. One-Factor ANOVA for Dependent Samples 18. Introduction to Factorial Designs 19. Describing Linear Relationships: Regression 20. Measuring the Strength of Linear Relationships: Correlation 21. Inferences From Nominal Data: The 2 Statistic 22. Introduction to Bayesian Statistics
01. Why Statistics? 02. Frequency Distributions and Percentiles 03. Central Tendency and Variability 04. z Scores and Normal Distributions 05. Overview of Inferential Statistics 06. Probability 07. Sampling Distributions 08. Logic of Hypothesis Testing 09. Power 10. Logic of Parameter Estimation 11. Inferences About Population Proportions Using the z Statistic 12. Inferences About When Is Unknown: The Single-sample t Test 13. Comparing Two Populations: Independent Samples 14. Random Sampling, Random Assignment, and Causality 15. Comparing Two Populations: Dependent Samples 16. Comparing Two Population Means: The Independent Sample ANOVA 17. One-Factor ANOVA for Dependent Samples 18. Introduction to Factorial Designs 19. Describing Linear Relationships: Regression 20. Measuring the Strength of Linear Relationships: Correlation 21. Inferences From Nominal Data: The 2 Statistic 22. Introduction to Bayesian Statistics
01. Why Statistics? 02. Frequency Distributions and Percentiles 03. Central Tendency and Variability 04. z Scores and Normal Distributions 05. Overview of Inferential Statistics 06. Probability 07. Sampling Distributions 08. Logic of Hypothesis Testing 09. Power 10. Logic of Parameter Estimation 11. Inferences About Population Proportions Using the z Statistic 12. Inferences About mi When s Is Unknown: The Single-sample t Test 13. Comparing Two Populations: Independent Samples 14. Random Sampling, Random Assignment, and Causality 15. Comparing Two Populations: Dependent Samples 16. Comparing Two Population Means: The Independent Sample ANOVA 17. One-Factor ANOVA for Dependent Samples 18. Introduction to Factorial Designs 19. Describing Linear Relationships: Regression 20. Measuring the Strength of Linear Relationships: Correlation 21. Inferences From Nominal Data: The 2 Statistic 22. Introduction to Bayesian Statistics
01. Why Statistics? 02. Frequency Distributions and Percentiles 03. Central Tendency and Variability 04. z Scores and Normal Distributions 05. Overview of Inferential Statistics 06. Probability 07. Sampling Distributions 08. Logic of Hypothesis Testing 09. Power 10. Logic of Parameter Estimation 11. Inferences About Population Proportions Using the z Statistic 12. Inferences About When Is Unknown: The Single-sample t Test 13. Comparing Two Populations: Independent Samples 14. Random Sampling, Random Assignment, and Causality 15. Comparing Two Populations: Dependent Samples 16. Comparing Two Population Means: The Independent Sample ANOVA 17. One-Factor ANOVA for Dependent Samples 18. Introduction to Factorial Designs 19. Describing Linear Relationships: Regression 20. Measuring the Strength of Linear Relationships: Correlation 21. Inferences From Nominal Data: The 2 Statistic 22. Introduction to Bayesian Statistics
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