How to think about statistics
著者
書誌事項
How to think about statistics
（A series of books in psychology）
W.H. Freeman, c1992
Rev. ed
- : hard
- : pbk
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注記
Includes bibliographical references (p. [193]-194) and index
内容説明・目次
- 巻冊次
-
: pbk ISBN 9780716722878
内容説明
Modern life is inundated with statistical data-polls, surveys, economic indicators, advertising claims, and research findings. You can't avoid the numbers, but you can learn to determine what they really mean - even if you suffer from "math-phobia." This revised edition offers a common sense method for understanding the statistics that affect your decisions and performance in business, in school, as a consumer, and as a voter. Rather than focus on mathematics and computations, this concise volume familiarizes the reader with the underlying logic of statistical analysis and problem-solving. It reveals how empirical studies are conceived, gathered, reported, interpreted-and sometimes obscured and distorted. The revised edition introduces fundamental concepts, using familiar, concrete examples; develops clearly the implications of those concepts; moves logically from one concept to the next, building a solid framework for interpreting statistical data; Includes numerous sample applications drawn from the fields of education, political science, psychology, social work, and sociology.
With dozens of refinements and a new organization, this revised edition should help the reader think critically about the statistical claims and arguments you encounter every day.
目次
- 1 Introduction: The Task
- The Basic Ideas
- Facing Mathphobia
- 2 Frequency Distributions: Normal Distributions
- Skewed Distributions
- Other Configurations
- Summary
- 3 Measures of Central Tendency: The Mean (X)
- The Median (Mdn)
- The Mode
- Summary
- Sample Applications
- 4 Measures of Variability: The Standard Deviation (S)
- The Interquartile Range
- The Range
- Degrees of Freedom
- Summary
- Sample Applications
- 5 Interpreting Individual Measures: Standard Scores: The z Scale
- Other Standard Scores
- Centile (or Percentile) Scores
- Age and Grade Norms
- Summary
- Sample Applications
- 6 Precision of Measurement
- Standard Errors
- Confidence Intervals and Levels of Confidence
- Effect of N on Standard Error
- Summary
- Sample Applications
- 7 Significance of a Difference between Two Means: An Example
- Test of Significance: The z Ratio
- Test of Significance: The t Ratio
- Significance Levels
- A Common Misinterpretation
- One- versus Two-Tail Tests
- Statistical versus Practical Significance
- Summary
- Sample Applications
- Contents 8 More on the Testing of Hypotheses: Comparison of Frequencies: Chi-Square
- Multimean Comparisons: Analysis of Variance
- Summary
- Sample Applications
- 9 Correlation: The Rank-Difference Coefficient (p)
- The Product-Moment Coefficient (r)
- Effect of Restricted Variability
- Standard Scores in Correlation
- A Matrix of Correlations
- Two Ways of Quantifying Reliability
- Expectancy Tables and Predictive Validity
- Reliability and Validity
- Summary
- Sample Applications
- 10 Correlation and Causation: Correlational versus Experimental Studies
- Continuous versus Discontinuous Variables and Measurements
- Correlation as an Index of Causation
- Summary
- 11 Summary: Appendix 1: Tests of Significance. Appendix II: List of Symbols.
- 巻冊次
-
: hard ISBN 9780716722885
内容説明
This edition of "How to Think About Statistics" offers a commonsense method for understanding the statistics that affect the reader's decisions and performance in business, in school, as a consumer, and as a voter. Rather than focus on mathematics and computations, this concise volume familiarizes the reader with the underlying logic of statistical analysis and problem-solving. It reveals how empirical studies are conceived, gathered, reported, interpreted - and sometimes obscured and distorted. This edition of the book introduces fundamental concepts, using familiar, concrete examples; develops clearly the implications of those concepts; moves logically from one concept to the next, building a solid framework for interpreting statistical data; includes numerous sample applications drawn from the fields of education, political science, psychology, social work, and sociology.
目次
- 1 Introduction: The Task
- The Basic Ideas
- Facing Mathphobia
- 2 Frequency Distributions: Normal Distributions
- Skewed Distributions
- Other Configurations
- Summary
- 3 Measures of Central Tendency: The Mean (X)
- The Median (Mdn)
- The Mode
- Summary
- Sample Applications
- 4 Measures of Variability: The Standard Deviation (S)
- The Interquartile Range
- The Range
- Degrees of Freedom
- Summary
- Sample Applications
- 5 Interpreting Individual Measures: Standard Scores: The z Scale
- Other Standard Scores
- Centile (or Percentile) Scores
- Age and Grade Norms
- Summary
- Sample Applications
- 6 Precision of Measurement
- Standard Errors
- Confidence Intervals and Levels of Confidence
- Effect of N on Standard Error
- Summary
- Sample Applications
- 7 Significance of a Difference between Two Means: An Example
- Test of Significance: The z Ratio
- Test of Significance: The t Ratio
- Significance Levels
- A Common Misinterpretation
- One- versus Two-Tail Tests
- Statistical versus Practical Significance
- Summary
- Sample Applications
- Contents 8 More on the Testing of Hypotheses: Comparison of Frequencies: Chi-Square
- Multimean Comparisons: Analysis of Variance
- Summary
- Sample Applications
- 9 Correlation: The Rank-Difference Coefficient (p)
- The Product-Moment Coefficient (r)
- Effect of Restricted Variability
- Standard Scores in Correlation
- A Matrix of Correlations
- Two Ways of Quantifying Reliability
- Expectancy Tables and Predictive Validity
- Reliability and Validity
- Summary
- Sample Applications
- 10 Correlation and Causation: Correlational versus Experimental Studies
- Continuous versus Discontinuous Variables and Measurements
- Correlation as an Index of Causation
- Summary
- 11 Summary: Appendix 1: Tests of Significance. Appendix II: List of Symbols.
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