Planning and Executing Credible Experiments - Robert J Moffat

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Biographie:

About the Authors xxi
Preface xxiii
Acknowledgments xxvii
About the Companion Website xxix

1 Choosing Credibility 1
1.1 The Responsibility of an Experimentalist 2
1.2 Losses of Credibility 2
1.3 Recovering Credibility 3
1.4 Starting with a Sharp Axe 3
1.5 A Systems View of Experimental Work 4
1.6 In Defense of Being a Generalist 5

2 The Nature of Experimental Work 7
2.1 Tested Guide of Strategy and Tactics 7
2.2 What Can Be Measured and What Cannot? 8
2.3 Beware Measuring Without Understanding: Warnings from History 12
2.4 How Does Experimental Work Differ from Theory and Analysis? 13
2.5 Uncertainty 23
2.6 Uncertainty Analysis 23

3 An Overview of Experiment Planning 27
3.1 Steps in an Experimental Plan 27
3.2 Iteration and Refinement 28
3.3 Risk Assessment/Risk Abatement 28
3.4 Questions to Guide Planning of an Experiment 29

4 Identifying the Motivating Question 31
4.1 The Prime Need 31
4.2 An Anchor and a Sieve 33
4.3 Identifying the Motivating Question Clarifies Thinking 33
4.4 Three Levels of Questions 35
4.5 Strong Inference 36
4.6 Agree on the Form of an Acceptable Answer 36
4.7 Specify the Allowable Uncertainty 37
4.8 Final Closure 37

5 Choosing the Approach 39
5.1 Laying Groundwork 39
5.2 Experiment Classifications 40
5.3 Real or Simplified Conditions? 43
5.4 Single-Sample or Multiple-Sample? 43
5.5 Statistical or Parametric Experiment Design? 45
5.6 Supportive or Refutative? 47
5.7 The Bottom Line 47

6 Mapping for Safety, Operation, and Results 51
6.1 Construct Multiple Maps to Illustrate and Guide Experiment Plan 51
6.2 Mapping Prior Work and Proposed Work 51
6.3 Mapping the Operable Domain of an Apparatus 53
6.4 Mapping in Operator's Coordinates 57
6.5 Mapping the Response Surface 59

7 Refreshing Statistics 65
7.1 Reviving Key Terms to Quantify Uncertainty 65
7.2 The Data Distribution Most Commonly Encountered The Normal Distribution for Samples of Infinite Size 71
7.3 Account for Small Samples: The t?-Distribution 72
7.4 Construct Simple Models by Computer to Explain the Data 73
7.5 Gain Confidence and Skill at Statistical Modeling Via the R Language 77
7.6 Report Uncertainty 80
7.7 Decrease Uncertainty (Improve Credibility) by Isolating Distinct Groups 81
7.8 Original Data, Summary, and R 82

8 Exploring Statistical Design of Experiments 87
8.1 Always Seeking Wiser Strategies 87
8.2 Evolving from Novice Experiment Design 87
8.3 Two?-Level and Three?-Level Factorial Experiment Plans 88
8.4 A Three?-Level, Three?-Factor Design 89
8.5 The Plackett-Burman 12?-Run Screening Design 93
8.6 Details About Analysis of Statistically Designed Experiments 95
8.7 Retrospect of Statistical Design Examples 101
8.8 Philosophy of Statistical Design 101
8.9 Statistical Design for Conditions That Challenge Factorial Designs 102
8.10 A Highly Recommended Tool for Statistical Design of Experiments 103
8.11 More Tools for Statistical Design of Experiments 103
8.12 Conclusion 103

9 Selecting the Data Points 107
9.1 The Three Categories of Data 107
9.2 Populating the Operating Volume 109
9.3 Example from Velocimetry 109
9.4 Organize the Data 112
9.5 Strategies to Select Next Data Points 114
9.6 Demonstrate Gosset for Selecting Data 120
9.7 Use Gosset to Analyze Results 133
9.8 Other Options and Features of Gosset 133
9.9 Using Gosset to Find Local Extrema in a Function of Several Variables 134
9.10 Summary 137

10 Analyzing Measurement U...