Abstract
Commonly accepted hypotheses guide practical determination of needed data quality; for example, as the probability that a decision uses data increases, the needed data quality increases, and the more rudimentary the uses of the data, the less data quality is needed. These hypotheses are formally defined and analyzed in some decision-theoretic models. Conditions under which the hypotheses hold and fail are examined. Particular attention is given to determining needed data quality when the users of the data behave nonoptimally.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 564-573 |
| Number of pages | 10 |
| Journal | Heterocycles |
| Volume | 80 |
| Issue number | 391 |
| DOIs | |
| State | Published - 1985 |
Funding
The author thanks Bob Boruch, Jerry Goldstein, and Jerry Sacks for helpful discussions and ideas, and an associate editor for correcting errors in Theorems 1 and 2. The author is deeply indebted to I. Richard Savage for suggesting the topic of the article, for key suggestions, and for invaluable comments on numerous drafts. Part of this research was supported by the Center for Economic Policy Research at Sanford University while the author was a visiting professor there.
Keywords
- Benefit-cost analysis
- Data use
- Decision theory
- Loss functions
- Morgenstern's hypothesis
- Sample size
ASJC Scopus subject areas
- Analytical Chemistry
- Pharmacology
- Organic Chemistry