Bernard Grofman is Distinguished Research Professor of Political Science and Social Psychology, School of Social Sciences, University of California, Irvine. A member of the American Academy of Arts and Science, he was the inaugural Jack W. Peltason Endowed Chair of Democracy Studies at UCI and has also been an Adjunct Professor of Economics at UCI and a visiting scholar-in-residence at universities in nearly a dozen countries.

This book is a game-changer for anyone learning about statistical sampling. Professor Grofman takes a subject that′s usually complex and math-heavy, and he makes it intuitive and accessible. The way he breaks down key ideas, provides hands-on Excel simulations, and captures core principles in his Introduction to the Laws of Statistical Sampling is truly brilliant. If you′re a student looking to really understand sampling - not just memorize formulas - this is the book for you. And if you′re an instructor searching for a fresh approach to bring sampling to life, look no further.

• Series Editor IntroductionAcknowledgementsAbout the AuthorChapter 1: An Overview1.1 Distinctive Features of the Approach to Sampling and Inference in This Volume1.2 The Structure of this Book1.3 Notation1.4 Basic MetricsAPPENDIX to Chapter 1: A Few Useful EXCEL Functions and ToolsChapter 2: Sampling Distributions2.1 Ideal Types of Univariate Data Distributions2.2 The Normal Distribution and the Standardized Normal Distribution2.3 Approximately Normal Distributions2.4 Cumulative Distributions and Finding Percentile Ranks Using EXCEL2.5 The Binomial Distribution2.6 The t-Distribution2.7 Other Approximately Normal Distributions2.8 Skewness and Kurtosis2.9 Not all Univariate Distributions are Approximately NormalAPPENDIX to Chapter 2: Theorem ProofsChapter 3: Sampling and Hypothesis Testing3.1 Sampling and Hypothesis Testing3.2 An Inventory of the Ten Laws of Statistical Sampling3.3 Sampling From a Normal Distribution with Binomial VarianceAPPENDIX to Chapter 3: Distinguishing the Standard Error of the Mean From the Sample ErrorChapter 4: Using EXCEL to Answer the First Five of our Six Questions4.1 Five Paradigmatic Questions About Sampling in Two-Candidate ElectionsChapter 5: Difference of Means5.1 Question 6. “When can we reject the claim that two distributions are drawn from the same population?”5.2 Experiments as the Basis for Generating Data for a Difference of Means Test5.3 Statistical Significance versus Substantive Significance: The Importance of Sample Size5.4 Illustrating Ideological Polarization and Partisan Sorting with Polling Data5.5 Warnings about Causation and Selection Bias EffectsChapter 6: Unifying Perspectives on Sampling and Hypothesis Testing Involving a Univariate Distribution6.1 Similarities Across Statistical Tools6.2 Concluding ThoughtsAPPENDIX 1 to Chapter 6 - Parallels Between the Ideas in this Book and Regression AnalysisAPPENDIX 2 to Chapter 6: A Short List of Suggestions for Further ReadingReferencesIndex

Bernard Grofman is Distinguished Research Professor of Political Science and Social Psychology, School of Social Sciences, University of California, Irvine. A member of the American Academy of Arts and Science, he was the inaugural Jack W. Peltason Endowed Chair of Democracy Studies at UCI and has also been an Adjunct Professor of Economics at UCI and a visiting scholar-in-residence at universities in nearly a dozen countries.

This book is a game-changer for anyone learning about statistical sampling. Professor Grofman takes a subject that′s usually complex and math-heavy, and he makes it intuitive and accessible. The way he breaks down key ideas, provides hands-on Excel simulations, and captures core principles in his Introduction to the Laws of Statistical Sampling is truly brilliant. If you′re a student looking to really understand sampling - not just memorize formulas - this is the book for you. And if you′re an instructor searching for a fresh approach to bring sampling to life, look no further.

• Series Editor IntroductionAcknowledgementsAbout the AuthorChapter 1: An Overview1.1 Distinctive Features of the Approach to Sampling and Inference in This Volume1.2 The Structure of this Book1.3 Notation1.4 Basic MetricsAPPENDIX to Chapter 1: A Few Useful EXCEL Functions and ToolsChapter 2: Sampling Distributions2.1 Ideal Types of Univariate Data Distributions2.2 The Normal Distribution and the Standardized Normal Distribution2.3 Approximately Normal Distributions2.4 Cumulative Distributions and Finding Percentile Ranks Using EXCEL2.5 The Binomial Distribution2.6 The t-Distribution2.7 Other Approximately Normal Distributions2.8 Skewness and Kurtosis2.9 Not all Univariate Distributions are Approximately NormalAPPENDIX to Chapter 2: Theorem ProofsChapter 3: Sampling and Hypothesis Testing3.1 Sampling and Hypothesis Testing3.2 An Inventory of the Ten Laws of Statistical Sampling3.3 Sampling From a Normal Distribution with Binomial VarianceAPPENDIX to Chapter 3: Distinguishing the Standard Error of the Mean From the Sample ErrorChapter 4: Using EXCEL to Answer the First Five of our Six Questions4.1 Five Paradigmatic Questions About Sampling in Two-Candidate ElectionsChapter 5: Difference of Means5.1 Question 6. “When can we reject the claim that two distributions are drawn from the same population?”5.2 Experiments as the Basis for Generating Data for a Difference of Means Test5.3 Statistical Significance versus Substantive Significance: The Importance of Sample Size5.4 Illustrating Ideological Polarization and Partisan Sorting with Polling Data5.5 Warnings about Causation and Selection Bias EffectsChapter 6: Unifying Perspectives on Sampling and Hypothesis Testing Involving a Univariate Distribution6.1 Similarities Across Statistical Tools6.2 Concluding ThoughtsAPPENDIX 1 to Chapter 6 - Parallels Between the Ideas in this Book and Regression AnalysisAPPENDIX 2 to Chapter 6: A Short List of Suggestions for Further ReadingReferencesIndex

Bernard Grofman is Distinguished Research Professor of Political Science and Social Psychology, School of Social Sciences, University of California, Irvine. A member of the American Academy of Arts and Science, he was the inaugural Jack W. Peltason Endowed Chair of Democracy Studies at UCI and has also been an Adjunct Professor of Economics at UCI and a visiting scholar-in-residence at universities in nearly a dozen countries.

This book is a game-changer for anyone learning about statistical sampling. Professor Grofman takes a subject that′s usually complex and math-heavy, and he makes it intuitive and accessible. The way he breaks down key ideas, provides hands-on Excel simulations, and captures core principles in his Introduction to the Laws of Statistical Sampling is truly brilliant. If you′re a student looking to really understand sampling - not just memorize formulas - this is the book for you. And if you′re an instructor searching for a fresh approach to bring sampling to life, look no further.

• Series Editor IntroductionAcknowledgementsAbout the AuthorChapter 1: An Overview1.1 Distinctive Features of the Approach to Sampling and Inference in This Volume1.2 The Structure of this Book1.3 Notation1.4 Basic MetricsAPPENDIX to Chapter 1: A Few Useful EXCEL Functions and ToolsChapter 2: Sampling Distributions2.1 Ideal Types of Univariate Data Distributions2.2 The Normal Distribution and the Standardized Normal Distribution2.3 Approximately Normal Distributions2.4 Cumulative Distributions and Finding Percentile Ranks Using EXCEL2.5 The Binomial Distribution2.6 The t-Distribution2.7 Other Approximately Normal Distributions2.8 Skewness and Kurtosis2.9 Not all Univariate Distributions are Approximately NormalAPPENDIX to Chapter 2: Theorem ProofsChapter 3: Sampling and Hypothesis Testing3.1 Sampling and Hypothesis Testing3.2 An Inventory of the Ten Laws of Statistical Sampling3.3 Sampling From a Normal Distribution with Binomial VarianceAPPENDIX to Chapter 3: Distinguishing the Standard Error of the Mean From the Sample ErrorChapter 4: Using EXCEL to Answer the First Five of our Six Questions4.1 Five Paradigmatic Questions About Sampling in Two-Candidate ElectionsChapter 5: Difference of Means5.1 Question 6. “When can we reject the claim that two distributions are drawn from the same population?”5.2 Experiments as the Basis for Generating Data for a Difference of Means Test5.3 Statistical Significance versus Substantive Significance: The Importance of Sample Size5.4 Illustrating Ideological Polarization and Partisan Sorting with Polling Data5.5 Warnings about Causation and Selection Bias EffectsChapter 6: Unifying Perspectives on Sampling and Hypothesis Testing Involving a Univariate Distribution6.1 Similarities Across Statistical Tools6.2 Concluding ThoughtsAPPENDIX 1 to Chapter 6 - Parallels Between the Ideas in this Book and Regression AnalysisAPPENDIX 2 to Chapter 6: A Short List of Suggestions for Further ReadingReferencesIndex