Chapter 7

Question 1

Big Data

Answer 1

Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software.

Question 2

Central Limit Theorom

Answer 2

A theorem that enables one to use the normal probability distribution to approximate the sampling distribution of a whenever the sample size is large.

Question 3

Cluster Sampling

Answer 3

A probability sampling method in which the population is first divided into clusters and then a simple random sample of the clusters is taken.

Question 4

Convenience Sampling

Answer 4

A nonprobability method of sampling whereby elements are selected for the sample on the basis of convenience.

Question 5

Coverage Error

Answer 5

Nonsampling error that results when the research objective and the population from which the sample is to be drawn are not aligned.

Question 6

Finite population correction factor

Answer 6

The term (I - n)/(N - 1) that is used in the formulas for on and Of whenever a finite population, rather than an infinite population, is being sampled. The generally accepted rule of thumb is to ignore the finite population correction factor whenever n/N <.05.

Question 7

Frame

Answer 7

A listing of the elements the sample will be selected from.

Question 8

Judgement Sampling

Answer 8

A nonprobability method of sampling whereby elements are selected for the sample based on the judgment of the person doing the study.

Question 9

Measurement Error

Answer 9

Nonsampling error that results from the incorrect or imprecise measurement of the population characteristic of interest.

Question 10

Nonresponse Error

Answer 10

Nonsampling error that results when potential respondents that belong to some segment(s) of the population are less likely to respond to the survey mechanism than potential respondents that belong to other segments of the population.

Question 11

Nonsampling Error

Answer 11

All types of errors other than sampling error, such as coverage error, nonresponse error, measurement error, interviewer error, and processing error.

Question 12

Parameter

Answer 12

A numerical characteristic of a population, such as a population mean M, a population standard deviation o, or a population proportion p.

Question 13

Point Estimate

Answer 13

The value of a point estimator used in a particular instance as an estimate of a population parameter.

Question 14

Point Estimator

Answer 14

The sample statistic, such as I, s, or p, that provides the point estimate of the population parameter.

Question 15

Random Sample

Answer 15

A random sample from an infinite population is a sample selected such that the following conditions are satisfied: (1) Each element selected comes from the same population; (2) each element is selected independently.

Question 16

Sample Statistic

Answer 16

A sample characteristic, such as a sample mean x, a sample standard deviation s, or a sample proportion p. The value of the sample statistic is used to estimate the value of the corresponding population parameter.

Question 17

Sampled Population

Answer 17

The population from which the sample is taken.

Question 18

Sampling Distribution

Answer 18

A probability distribution consisting of all possible values of a sample statistic.

Question 19

Sampling Error

Answer 19

The error that occurs because a sample, and not the entire population, is used to estimate a population parameter.

Question 20

Simple Random Sample

Answer 20

A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size n has the same probability of being selected.

Question 21

Standard Error

Answer 21

The standard deviation of a point estimator.

Question 22

Stratified Random Sampling

Answer 22

A probability sampling method in which the population is first divided into strata and a simple random sample is then taken from each stratum.

Question 23

Systematic Sampling

Answer 23

A probability sampling method in which we randomly select one of the first k elements and then select every kth element thereafter.

Question 24

Tall Data

Answer 24

A data set that has so many observations that traditional statistical inference has little meaning.

Question 25

Target Population

Answer 25

The population for which statistical inferences such as point estimates are made. It is important for the target population to correspond as closely as possible to the sampled population.

Question 26

Unbiased

Answer 26

A property of a point estimator that is present when the expected value of the point estimator is equal to the population parameter it estimates.

Question 27

Variety

Answer 27

The diversity in types and structures of the data generated.

Question 28

Velocity

Answer 28

The speed at which the data are generated.

Question 29

Veracity

Answer 29

The reliability of the data generated.

Question 30

Volume

Answer 30

The amount of data generated.

Question 31

Wide Data

Answer 31

A data set that has so many variables that simultaneous consideration of all variables is infeasible.