Statistics Terms Chapter 1 - Sampling and Data

Convenience Sampling

A nonrandom method of selecting a sample; this method selects individuals that are easily accessible and may result in biased data.

Probability

A number between zero and one, inclusive, that gives the likelihood that a specific event will occur.

Parameter

A number that is used to represent a population characteristic and that generally cannot be determined easily.

Statistic

A numerical characteristic of the sample; a statistic estimates the corresponding population parameter.

Discrete Random Variable

A random variable (RV) whose outcomes are counted

Continuous Random Variable

A random variable (RV) whose outcomes are measured; the height of trees in the forest is a continuous RV.

Variable

A characteristic of interest for each person or object in a population.

Control Group

A group in a randomized experiment that receives an inactive treatment but is otherwise managed exactly as the other groups.

Sampling without Replacement

A member of the population may be chosen for inclusion in a sample only once. If chosen, the member is not returned to the population before the next selection.

Cluster Sampling

A method for selecting a random sample and dividing the population into groups (clusters); use simple random sampling to select a set of clusters. Every individual in the chosen clusters is included in the sample.

Stratified Sampling

A method for selecting a random sample used to ensure that subgroups of the population are represented adequately: divide the population into groups (strata); use simple random sampling to identify a proportionate number of individuals from each stratum.

Systematic Sampling

A method for selecting a random sample: list the members of the population; use simple random sampling to select a starting point in the population; let k = (number of individuals in the population)/(number of individuals needed in the sample); choose every kth individual in the list starting with the one that was randomly selected. If necessary, return to the beginning of the population list to complete your sample.

Random Sampling

A method of selecting a sample that gives every member of the population an equal chance of being selected.

Data

A set of observations (a set of possible outcomes); most data can be put into two groups: qualitative or quantitative.

Simple Random Sampling

A straightforward method for selecting a random sample: give each member of the population a number; use a random number generator select a set of labels; these randomly selected labels identify the members of your sample.

Sample

A subset of the population being studied.

Representative Sample

A subset of the population that has the same characteristics as the population.

Lurking Variable

A variable that has an effect on a study even though it is neither an explanatory variable nor a response variable.

Population

All individuals, objects, or measurements whose properties are being studied.

Average

Also called mean; a number that describes the central tendency of the data.

Placebo

An inactive treatment that has no real effect on the explanatory variable.

Nonsampling Error

An issue that affects the reliability of sampling data other than natural variation; it includes a variety of human errors including poor study design, biased sampling methods, inaccurate information provided by study participants, data entry errors, and poor analysis.

Informed Consent

Any human subject in a research study must be cognizant of any risks or costs associated with the study. The subject has the right to know the nature of the treatments included in the study, their potential risks, and their potential benefits. Consent must be given freely by an informed, fit participant.

Experimental Unit

Any individual or object to be measured.

Qualitative Data

Data in which each data value falls into a particular category. Often referred to as categorical data.

Quantitative Continuous Data

Data is continuous if it is the result of measuring (such as distance traveled or weight of luggage)

Quantitative Discrete Data

Data is discrete if it is the result of counting (such as the number of students of a given ethnic group in a class or the number of books on a shelf).

Quantitative Data

Data that consists of numeric values, which are the result of measuring or counting (often referred to as numerical data). Can be discrete or continuous.

Treatments

Different values or components of the explanatory variable applied in an experiment.

Sampling Bias

Not all members of the population are equally likely to be selected.

Blinding

Not telling participants which treatment a subject is receiving.

Sampling with Replacement

Once a member of the population is selected for inclusion in a sample, that member is returned to the population for the selection of the next individual.

Double-blinding

The act of blinding both the subjects of an experiment and the researchers who work with the subjects.

Random Assignment

The act of organizing experimental units into treatment groups using random methods.

Response Variable

The dependent variable in an experiment; the value that is measured for change at the end of an experiment.

Explanatory Variable

The independent variable in an experiment; the value controlled by researchers.

Sampling Error

The natural variation that results from selecting a sample to represent a larger population; this variation decreases as the sample size increases, so selecting larger samples reduces sampling error.

Proportion

The number of successes divided by the total number in the sample.

Explanatory Variable Frequency

The number of times a value of the data occurs.

Relative Frequency

The ratio of the number of times a value of the data occurs in the set of all outcomes to the total number of outcomes

Cumulative Relative Frequency

The term applies to an ordered set of observations from smallest to largest. The cumulative relative frequency is the sum of the relative frequencies for all values that are less than or equal to the given value.

Numerical Variable

Variables that take on values that are indicated by numbers.

Categorical Variable

Variables that take on values that are names or labels.