MAT 207 Chapter 1 Terms and Definitions

Parameter

A numerical measurement describing some characteristic of a population.

Statistic

A numerical measurement describing some characteristic of a sample.

Quantitative ( or numerical) data

This type of data consists of numbers representing counts or measurements.

Categorical (or qualitative or attribute) data

This type of data consists of names or labels (not numbers that represent counts or measurements).

Discrete Data

Result when the data values are quantitative and the number of values is finite, or “countable”

Continuous (numerical) data

Result from infinitely many possible quantitative values, where the collection of values is not countable.

Nominal Measurement

Categories only

Ordinal Measurements

Categories with some order

Interval Measurement

Differences but no natural zero point

Ratio Measurement

Differences and a natural zero point

Big data

Refers to data sets so large and so complex that their analysis is beyond the capabilities of traditional software tools.

Ways of Correcting for Missing Data:

Delete Cases and Impute Missing Values

Delete Cases

One very common method for dealing with missing data is to delete all subjects having any missing values.

Impute Missing Values

We “impute” missing data values when we substitute values for them.

Replication

The repetition of an experiment on more than one individual.

Blinding

A technique in which the subject doesn’t know whether he or she is receiving a treatment or a placebo.

Simple Random Sample

A sample of n subjects is selected in a way that every possible sample of the same size n has the same chance of being chosen.

Systematic Sampling

Select some starting point and then select every kth element in the population.

Stratified Sampling

Subdivide the population into at least two different subgroups (or strata) so that the subject within the same subgroup share the same characteristics.

Cluster Sampling

Divide the population area into sections (or clusters), then randomly select some of those clusters, and choose all the members from those selected clusters.

Multistage Sampling

Collects data by using some combination of the basic sampling methods.

Cross-sectional study

Data are observed, measured, and collected at one point in time, not over a period of time.

Retrospective study

Data are collected from a past time period by going back in time.

Prospective study

Data are collected in the future from groups sharing common factors.

Confounding

This occurs when we see some effect, but can’t identify the specific factor that caused it.

Completely Randomized Experimental Design

Assign subjects to different treatment groups through a process of random selection.

Randomized Block Design

A group of subjects that are similar, but blocks differ in ways that might affect the outcome of the experiment.M

Matched Pairs Design

Compare two treatment groups by using subjects matched in pairs that are somehow related or have similar characteristics.

Rigorously Controlled Design

Carefully assigns subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment.

Sampling error

This occurs when the sample has been selected with a random method, but there is a discrepancy between a sample result and the true population result.

Nonsampling error

The result of human error, including such factors as wrong data entries, computing errors, questions with biased wording, false data provided by respondents.

Nonrandom sampling error

The result of using a sampling method that is not random, such as using a convenience sample or a voluntary response sample.