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Glossary

Guidance for the Data Quality Objectives Process EPA QA/G-4 Final, September 1994 Appendix D - Glossary of Terms.

action level:

the numerical value that causes the decision maker to choose one of the alternative actions (e.g., compliance or noncompliance). It may be a regulatory threshold standard, such as a Maximum Contaminant Level for drinking water; a risk-based concentration level; a technological limitation; or a reference-based standard. [Note: the action level is specified during the planning phase of a data collection activity; it is not calculated from the sampling data.]

alternative hypothesis:

see hypothesis.

bias:

the systematic or persistent distortion of a measurement process which causes errors in one direction (i.e., the expected sample measurement is different than the sample's true value).

boundaries:

the spatial and temporal conditions and practical constraints under which environmental data are collected. Boundaries specify the area or volume (spatial boundary) and the time period (temporal boundary) to which the decision will apply. Samples are then collected within these boundaries.

data collection design:

a data collection design specifies the configuration of the environmental monitoring effort to satisfy the DQOs. It includes the types of samples or monitoring information to be collected; where, when, and under what conditions they should be collected; what variables are to be measured; and the Quality Assurance and Quality Control (QA/QC) components that ensure acceptable sampling design error and measurement error to meet the decision error rates specified in the DQOs. The data collection design is the principal part of the QAPP.

Data Quality Assessment (DQA) Process:

a statistical and scientific evaluation of the data set to assess the validity and performance of the data collection design and statistical test, and to establish whether a data set is adequate for its intended use.

Data Quality Objectives (DQOs):

qualitative and quantitative statements derived from the DQO Process that clarify study objectives, define the appropriate type of data, and specify the tolerable levels of potential decision errors that will be used as the basis for establishing the quality and quantity of data needed to support decisions.

Data Quality Objectives Process:

a Quality Management tool based on the Scientific Method, developed by the U.S. Environmental Protection Agency to facilitate the planning of environmental data collection activities. The DQO Process enables planners to focus their planning efforts by specifying the intended use of the data (the decision), the decision criteria (action level) and the decision maker's tolerable decision error rates. The products of the DQO Process are the DQOs.

decision error:

an error made when drawing an inference from data in the context of hypothesis testing, such that variability or bias in the data mislead the decision maker to draw a conclusion that is inconsistent with the true or actual state of the population under study. See also false negative decision error, false positive decision error.

defensible:

the ability to withstand any reasonable challenge related to the veracity, integrity, or quality of the logical, technical, or scientific approach taken in a decision making process.

false negative decision error:

a false negative decision error occurs when the decision maker does not reject the null hypothesis when the null hypothesis actually is false. In statistical terminology, a false negative decision error is also called a Type II error. The measure of the size of the error is expressed as a probability, usually referred to as "beta"; this probability is also called the complement of power.

false positive decision error:

a false positive decision error occurs when a decision maker rejects the null hypothesis when the null hypothesis actually is true. In statistical terminology, a false positive decision error is also called a Type I error. The measure of the size of the error is expressed as a probability, usually referred to as "alpha", the "level of significance," or "size of the critical region."

gray region:

a range of values of the population parameter of interest (such as mean contaminant concentration) where the consequences of making a decision error are relatively minor. The gray region is bounded on one side by the action level.

hypothesis:

a tentative assumption made to draw out and test its logical or empirical consequences. In hypothesis testing, the hypothesis is labeled "null" or "alternative", depending on the decision maker's concerns for making a decision error.

limits on decision errors:

the tolerable decision error probabilities established by the decision maker. Potential economic, health, ecological, political, and social consequences of decision errors should be considered when setting the limits.

mean:

(i) a measure of central tendency of the population (population mean), or (ii) the arithmetic average of a set of values (sample mean).

measurement error:

the difference between the true or actual state and that which is reported from measurements.

median:

the middle value for an ordered set of n values; represented by the central value when n is odd or by the average of the two most central values when n is even. The median is the 50th percentile.

medium:

a substance (e.g., air, water, soil) which serves as a carrier of the analytes of interest.

natural variability:

the variability that is inherent or natural to the media, objects, or people being studied.

null hypothesis:

see hypothesis.

parameter:

a numerical descriptive measure of a population.

percentile:

the specific value of a distribution that divides the distribution such that p percent of the distribution is equal to or below that value. Example for p=95: "The 95th percentile is X" means that 95% of the values in the population (or statistical sample) are less than or equal to X.

planning team:

the group of people that will carry out the DQO Process. Members include the decision maker (senior manager), representatives of other data users, senior program and technical staff, someone with statistical expertise, and a QA/QC advisor (such as a QA Manager).

population:

the total collection of objects, media, or people to be studied and from which a sample is to be drawn.

power function:

the probability of rejecting the null hypothesis (Ho) over the range of possible population parameter values. The power function is used to assess the goodness of a hypothesis test or to compare two competing tests.

quality assurance (QA):

an integrated system of management activities involving planning, quality control, quality assessment, reporting, and quality improvement to ensure that a product or service (e.g., environmental data) meets defined standards of quality with a stated level of confidence.

Quality Assurance Project Plan (QAPP):

a formal technical document containing the detailed QA, QC and other technical procedures for assuring the quality of environmental data prepared for each EPA environmental data collection activity and approved prior to collecting the data.

Quality Control (QC):

the overall system of technical activities that measures the attributes and performance of a process, item, or service against defined standards to verify that they meet the stated requirements established by the customer.

Quality Management Plan (QMP):

a formal document describing the management policies, objectives, principles, organizational authority, responsibilities, accountability, and implementation protocols of an agency, organization, or laboratory for ensuring quality in its products and utility to its users. In EPA, QMPs are submitted to the Quality Assurance Management Staff (QAMS) for approval.

range:

the numerical difference between the minimum and maximum of a set of values.

sample (1):

a single item or specimen from a larger whole or group, such as any single sample of any medium (air, water, soil, etc.).

sample (2):

a set of individual samples (specimens or readings), drawn from a population, whose properties are studied to gain information about the whole.

sampling:

the process of obtaining representative samples and/or measurements of a subset of a population.

sampling design error:

the error due to observing only a limited number of the total possible values that make up the population being studied. It should be distinguished from errors due to imperfect selection; bias in response; and errors of observation, measurement, or recording, etc.

scientific method:

the principles and processes regarded as necessaryfor scientific investigation, including rules for concept or hypothesis formulation, conduct of experiments, and validation of hypotheses by analysis of observations.

standard deviation:

the square root of the variance.

statistic:

a function of the sample measurements; e.g., the sample mean or standard deviation.

statistical test:

any statistical method that is used to determine which of several hypotheses are true.

total study error:

the combination of sampling design error and measurement error.

true:

being in accord with the actual state of affairs.

Type I error:

a Type I error occurs when a decision maker rejects the null hypothesis when it is actually true. See false positive decision error.

Type II error:

a Type II error occurs when the decision maker fails to reject the null hypothesis when it is actually false. See false negative decision error.

variable:

the attribute of the environment that is indeterminant.

variance:

a measure of (i) the variability or dispersion in a population (population variance), or (ii) the sum of the squared deviations of the measurements about their mean divided by the degrees of freedom (sample variance).

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