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- StudyBlue
- Utah
- Brigham Young University
- Statistics
- Statistics 121
- Christensen
- Key Terms Exam 1

Lexie B.

Confounding

A situation where the effect of one variable on the response variable cannot be separated from the

effect of another variable on the response variable.

Distribution

A list or a graph that shows the possible values of a variable together with the frequency of each value.

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IQR

a measure of variability recommended for skewed data or data with outliers; computed

as IQR = Q3 – Q1

Lurking Variable

A variable that has an important effect on the relationship among the variables in a study but is not taken into account.

Median

A measure of the center of data; it‘s the point such that half the numbers are smaller and the other half are

larger (the midpoint of the ordered data set).

Mean

A measure of the center of the data.; it‘s the point that ―balances‖ the data.

Probability Sample

a sample selected using a random device where each individual in the population has a chance

(doesn‘t have to be equal) of being selected. Probability samples are necessary for making inferences. Examples

include: SRS, stratified and multistage

Q1

A location measure of the data such that has one fourth or 25% of the data is smaller than it. Found by dividing

the ordered data set in half (excluding the middle observation if n is odd) and finding the median of the lower

half of the data.

Q3

: A location measure of the data that has three-fourths or 75% of the data is smaller than it. Found by dividing

the ordered data set in half (excluding the middle observation if n is odd) and finding the median of the upper

half of the data.

Randomization

A method of assigning individuals in an experiment to treatment groups using some random

device that eliminates bias and gives each unit the same probability of being assigned to any treatment group.

Randomization ―balances‖ the treatment groups, thus averaging out lurking and extraneous variables. Allows us

to use the laws of probability to make inferences. Randomization as a condition can be SRS or RAT (Random

allocation to Treatments.)

Replication

Having more than one individual in each treatment group. Replication is necessary for measuring

variability. Also, the greater the replication, the more precise the results.

Simple Random Sample

A sample of size n selected from the population in such a way that each possible sample

of size n has an equal chance of being selected.

Standard Deviation

A measure of the ―average‖ or typical deviation of the observations about the mean; measures

variability of data about the mean.

Statistically Significant Results

Results of a study that differ too much from what we expected to attribute to chance

variation alone.

Z-Score

A measure of the number of standard deviations a value or observation is from the mean, a standardized

value.

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Blocking

The grouping of individuals according to some characteristic like rats in the same litter or plots of land

at the same location. The random allocation is carried out separately within each group.

Response Variable

: A variable that gives the result (may not be a number) of the outcome of a study; measured on

an individual.

Explanatory Variable

A variable that may or may not explain the outcomes (responses) of a study. It is described

using a phrase that describes all possible treatments. Note: An observational study can have an explanatory

variable, but a valid experiment always has an explanatory variable.

Individual

SUBJECT

Stratified Sampling

Seperate groups called Strata pick from every group, EXMPLE.. freshman, sophomores, juniors, seniors.

Multistage

SRS within SRS Example, pick five states- then ten counties- then 10 people.

Exmeriment 1 Competely Randomized

Like SRS Randomly ASSIGN

Experiemet 2 Randomized Block Design

Block is like a strata. Example, 50 people, divide into male and female, random assign each.

Experiment 3 Matched Pairs

Everyone gets BOTH treatments, or twins.

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