tailieunhanh - Environmental Forensics: Principles and Applications - Chapter 3

3 Identification of Biased Environmental Data Coincidence, error, studied ignorance, or junk science? INTRODUCTION An expert opinion is worth no more than the factual data upon which it is based. The critical review of environmental data is therefore essential for judging the reliability of the factual information. Environmental data relied upon to form an opinion should be of a sufficient known quality to withstand the scientific and legal challenges relative to the purpose of the data collection. In most instances, only a small percentage (about 10 to 15%) of the data in an environmental investigation contains elements susceptible to bias. These elements are. | 3 Identification of Biased Environmental Data Coincidence error studied ignorance or junk science INTRODUCTION An expert opinion is worth no more than the factual data upon which it is based. The critical review of environmental data is therefore essential for judging the reliability of the factual information. Environmental data relied upon to form an opinion should be of a sufficient known quality to withstand the scientific and legal challenges relative to the purpose of the data collection. In most instances only a small percentage about 10 to 15 of the data in an environmental investigation contains elements susceptible to bias. These elements are usually associated with the geologic investigation and sample collection analytical testing and interpretation of the horizontal and vertical extent of soil and groundwater contamination. An important task in the forensic review of environmental data is the determination of whether a pattern of bias systematic error exists. This bias can be due to factually incorrect information errors or intentional manipulation. Figure illustrates bias and data variability random error based on a sample population whose true concentration is about 20 parts per million ppm . As depicted in Figure data can be biased negatively or positively. Three specific types of biases and or errors are defined as follows 1. Positive bias In a data sufficiency context a positive bias arises when a test incorrectly indicates contamination or an increase in contamination when there is none. 2. Negative bias In a data sufficiency context a negative bias occurs when monitoring fails to detect contamination or an increase in the concentration of a hazardous material. 3. Erratic data Erratic data are anomalous values which make it statistically impossible to develop meaningful trends and or correlations. These biases result from investigative sampling analytical and statistical errors. Ultimately expert witness opinions based on incorrect .