Statistical considerations of the random selection process in a drug testing program
In a prospective drug testing program, individuals whose job classifications have been defined as sensitive are placed in a selection pool. On a periodic basis, individuals are chosen from this pool for drug testing. Random selection is a fair and impartial approach. A random selection process generates a Poisson distribution of probabilities that can be used to predict how many times an individual will be selected during a specific time interval. This information can be used to model the selection part of a drug testing program to determine whether specific conditions of testing are met. For example, the probability of being selected a given number of times during the testing period can be minimized or maximized by varying the frequency of the sampling process. Consequently, the Poisson distribution and the mathematics governing it can be used to structure a drug testing program to meet the needs and dictates of any given situation.
- Research Organization:
- Oak Ridge National Lab., TN (USA); Oak Ridge Gaseous Diffusion Plant, TN (USA); Oak Ridge Y-12 Plant, TN (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6765052
- Report Number(s):
- CONF-870141-1; ON: DE87005322
- Resource Relation:
- Conference: 10. annual Arnold O. Beckman conference in clinical chemistry, Newport Beach, CA, USA, 18 Jan 1987; Other Information: Portions of this document are illegible in microfiche products
- Country of Publication:
- United States
- Language:
- English
Similar Records
Field evaluation of remote wind sensing technologies: Shore-based and buoy mounted LIDAR systems
TRAN-STAT statistics for environmental studies, issue No. 24, August 1983. Field sampling designs, simple random and stratified random sampling