How to Discount Double-Counting When It Counts
Some Clarifications
Bibliographic Data
| ID | 8396272 |
|---|---|
| Authors | Deborah G Mayo (0000-0001-8252-9968, Virginia Tech, corresponding author) |
| Year | 2008 |
| Volume | 59 |
| Issue | 4 |
| Pages | 857-879 |
| Publication date | 2008-12-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axn034 |
| OpenAlex | W2155009771 |
| Language | EN |
| Citations received | 10 |
| References cited | 7 |
The issues of double-counting, use-constructing, and selection effects have long been the subject of debate in the philosophical as well as statistical literature. I have argued that it is the severity, stringency, or probativeness of the test—or lack of it—that should determine if a double-use of data is admissible. Hitchcock and Sober ([2004]) question whether this ‘severity criterion' can perform its intended job. I argue that their criticisms stem from a flawed interpretation of the severity criterion. Taking their criticism as a springboard, I elucidate some of the central examples that have long been controversial, and clarify how the severity criterion is properly applied to them.1. Severity and Use-Constructing: Four Points (and Some Clarificatory Notes) 1.1. Point 1: Getting beyond ‘all or nothing’ standpoints1.2. Point 2: The rationale for prohibiting double-counting is the requirement that tests be severe1.3. Point 3: Evaluate severity of a test T by its associated construction rule R1.4. Point 4: The ease of passing vs. ease of erroneous passing: Statistical vs. ‘Definitional’ probability2. The False Dilemma: Hitchcock and Sober 2.1. Marsha measures her desk reliably2.2. A false dilemma3. Canonical Errors of Inference 3.1. How construction rules may alter the error-probing performance of tests3.2. Rules for accounting for anomalies3.3. Hunting for statistically significant differences4. Concluding Remarks
Criticism · Desk · Dilemma · Epistemology · Inference · Mathematical economics · Nothing · Point (geometry) · Selection (genetic algorithm) · Statistical hypothesis testing · Statistics · Subject (documents) · Test (biology) · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Explainable Artificial Intelligence (XAI · Law · Mathematics · Philosophy · Philosophy and History of Science · Psychology
A Problem of 'Relevance'
Prediction in Selectionist Evolutionary Theory
Statistical Inference as Severe Testing
Preregistration does not improve the transparent evaluation of severity in Popper’s philosophy of science or when deviations are allowed
The perils of tweaking
Some surprising facts about (the problem of) surprising facts
Some Methodological Issues in Experimental Economics
Severe Testing
The Costs of Harking
Model-Selection Theory
| Unique citing works | 10 |
|---|---|
| Citations per year | 0,56 |
| Citation span | 2008 - 2026 (19) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |