Understanding Scientific Reasoning

ISBN-10: 015506326X
ISBN-13: 9780155063266
Edition: 5th 2006
List price: $152.95 Buy it from $148.85
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Description: Not everything that claims to be science is. UNDERSTANDING SCIENTIFIC REASONING shows you easy-to-use principles that let you distinguish good science from bad information you encounter in both textbooks and the popular media. And because it uses  More...

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Book details

List price: $152.95
Edition: 5th
Copyright year: 2006
Publisher: Wadsworth
Publication date: 7/13/2005
Binding: Paperback
Pages: 308
Size: 6.25" wide x 9.00" long x 0.75" tall
Weight: 2.090
Language: English

Not everything that claims to be science is. UNDERSTANDING SCIENTIFIC REASONING shows you easy-to-use principles that let you distinguish good science from bad information you encounter in both textbooks and the popular media. And because it uses the same processes that scientists use (but simplified), you'll know you're getting the most reliable instruction around. You'll also learn how to reason through case studies using the same informal logic skills employed by scientists.

Preface
Acknowledgments
Why Understand Scientific Reasoning?
Why Study Scientific Reasoning?
Some Preliminary Examples
The Expanding Universe
Global Warming
Cigarette Smoking and Coronary Heart Disease
Summary
How to Study Scientific Reasoning
How Should We Understand Scientific Reasoning?
General Strategy
A Computer Analogy
Tactics
Reasoning as a Skill
Theoretical Hypotheses
Understanding and Evaluating Theoretical Hypotheses
The Double Helix: A Case Study
The Three-Chain Model
The Two-Chain Model
Understanding Episodes in Science
The Human Context of Science
Exploring How the World Works
Finding a Problem
Constructing Models
Deciding Whether a Model Fits
Convincing Others
Spreading the Word
Models and Theories
Scale Models
Analog Models
Models and Maps
Theoretical Models
Theoretical Hypotheses
Theories
Data from the Real World
Predictions from Models
The Components of a Scientific Episode
Evaluating Theoretical Hypotheses
Evidence That a Model Does Not Fit the Real World
Evidence That a Model Does Fit the Real World
A Program for Evaluating Theoretical Hypotheses
Why the Program Works
How the Program Works: Three Examples
A Case of Negative Evidence
A Case of Positive Evidence
A Case of Inconclusive Data
Crucial Experiments
The Structure of Crucial Experiments
Analysis of a Crucial Experiment
Model Development
A Case of Model Development
Exercises
Historical Episodes
The Phases of Venus
Isaac Newton and Halley's Comet
Newtonian Models
Halley's Comet
The Downfall of the Phlogiston Theory
Darwin and Evolution
Special Creation
Darwin's Finches
Darwin's Model of Evolutionary Development
Mendelian Genetics
Mendel's Original Experiments
Mendel's Model
The Backcross Test
The Revolution in Geology
Mobilism
Seafloor Spreading
Magnetism, Geomagnetism, and Paleomagnetism
Exercises
Marginal Science
Freudian Psychology
Freud's Theory
The Case of Little Hans
Freud's Hypothesis
Astrology
The Predictions Are Vague
There Are Multiple Predictions
Astrology as an Interpretative Framework
Extraterrestrial Visitation
An Alternative Model
What about the Data?
Reincarnation
In Search of Bridey Murphy
Extrasensory Perception
Clairvoyance
Speaking of Probability
The Burden of Proof
Exercises
Statistical and Causal Hypotheses
Statistical Models and Probability
Why Statistical and Probabilistic Models are Important
The Elements of a Statistical Study
The Real-World Population
The Sample
A Model of the Population
A Model of the Sample
Putting the Elements Together
Proportions and Distributions
Proportions
Variables
Distributions
Simple Correlations
Variables not Correlated
Variables Correlated
Summary
Symmetry of Correlations
Strength of Correlations
Probability Models
A Probability Model
Simple Addition Rule
Simple Multiplication Rule
Conditional Probabilities
The Flippant Judge
The Problem
The Solution
Sampling
Replacement
Random Sampling: Two Trials
Three, Four, and Five Trials
Large Samples
Expected Frequency
Standard Deviation
The Value of Large Samples
Sampling with Unequal Probabilities
Appendix
Correlation Coefficient
Exercises
Evaluating Statistical Hypotheses
Estimation
Margin of Error
Sample Size and Margin of Error
Confidence Level and Margin of Error
Sample Frequency and Margin of Error
Evaluating Distributions and Correlations
Evaluating Distributions
Evaluating Correlations
Evidence for a Correlation
Lack of Evidence for a Correlation
Estimating the Strength of a Correlation
Confidence Levels for Correlations
Statistical Significance
Statistically Significant Differences
Statistical Significance and Strength of Correlations
Evaluating Correlations: a Quick and Dirty Method
Survey Sampling
The 1997-98 Health Behavior in School-Aged Children (HBSC) World Health Organization (WHO) Cross-National Survey
How the Survey was Carried Out
Selected Data
Evaluating Statistical Hypotheses
The Real-World Population
Sample Data
A Statistical Model
Random Sampling
Evaluating the Hypothesis
Summing Up
A Program for Evaluating Statistical Hypotheses
Physical Fighting
Weapon Carrying in Self-Defense
Problems with Survey Sampling
Nonrandom Sampling
Unreliability of Information
The Exit Polls Discrepancy in the 2004 U.S. Presidential Election
Appendix
Formula for Margin of Error
Formula for Statistically Significant Differences
Formula for Correctly Adding Margins of Error
Exercises
Causal Models
Correlation and Causation
Correlation without Causation
Causation is not Symmetric
Causal Production
Causal Models for Individuals
A Deterministic Model
Could Humans be Deterministic Systems?
A Probabilistic Model
Causal Models for Populations
A Comparative Model for Causation in Populations
What if the Individuals are Probabilistic?
Effectiveness of Causal Factors
Effectiveness in Individuals
Effectiveness in Populations
Summary: How Causation Differs from Correlation
Exercises
Evaluating Causal Hypotheses
Saccharin and Cancer
Randomized Experimental Designs
Real-World Population and Causal Hypothesis
Sample Data
Experimental Design
Random Sampling
Evaluating the Causal Hypothesis
Program for Evaluating Causal Hypotheses
Of Rats and Humans
Double-Blind Studies
Schizophrenia and Heredity
Combined Results from Five Recent Twin Studies
Prospective Designs
Controlling for Other Variables
Prospective versus Randomized Experimental Designs
Schizophrenia Etiology and the Dystrobrevin-Binding Protein 1 (DTNBP1) Gene
Retrospective Designs
Nonrandom Selection of the Experimental Group
Constructing the Control Group
Controlling for Other Variables
Retrospective Studies and Survey Sampling
Effectiveness and Retrospective Studies
Evaluating Retrospective Studies
Statistical Evidence for Causal Hypotheses
Can Prospective Studies Prove a Causal Connection?
The Ethics of Experimental Design
New Trends in Biomedical Research
Summary
Exercises
Knowledge, Values, and Decisions
Models of Decision Making
Options
States of The World
Outcomes
Values
Ranking Values
Measuring Values
Scientific Knowledge and Decision Strategies
Decision Making with Certainty
Highest Value Strategy
Decision Making with Complete Uncertainty
Better and Worse Options
Best Options
Satisfactory Options
Playing It Safe
Gambling
Gambling versus Playing It Safe
Decision Making with Risk
Expected Value
Is the Expected-Value Strategy the Best Strategy?
Modern Utility Theory
Ramsey's Insight
A Fictional Case
Maximizing Expected Utility
Von Neumann-Morgenstern Utility Theory
Another Fictional Case
Summary of Decision Strategies
Exercises
Evaluating Decisions
A Program for Evaluating Decisions
Decisions Involving Low Probabilities
Oral Contraceptives and Breast Cancer
Decisions Involving Moderate Probabilities
The Smoking Decision
Further Considerations
Confronting Your Values
Policy Decisions with Uncertainty
Global Warming
Whose Decision is It, Anyway?
Exercises
Index
Credits

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