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# Statistics49 cards

*Tagged as: nursing,
marketing,
music,
biology,
medical,
medicine,
pharmacology
*

1

## The science of statistics is

Collecting
Organizing
Summarizing
Analyzing
information to draw conclusions or answer questions

2

## Anecdotal claims, as opposed to statistics, are

Conclusions based on very little data
Stories and rumors

3

## Data can be misused when

Data is incorrectly obtained
Data is incorrectly analyzed

4

## Good statistics should

Understand the difference between direct and indirect (lurking variable) relations
Understand the impacts of variability

5

## Mathematics

Solves problems with 100% certainty
Has only one correct answer

6

## Statistics, because of variability

Does not solve problems with 100% certainty (95% certainty is much more common)
Frequently has multiple reasonable answers

7

## A population

Is the group to be studied
Includes all of the individuals in the group

8

## A sample

Is a subset of the population
Is often used in analyses because getting access to the entire population is impractical

9

## Identify the research objective

What questions are to be answered?
What group should be studied?

10

## Collect the information needed

Can you access the entire population?
How can you collect a good sample?
What other methods are available and appropriate?

11

## Organize and summarize the information

Descriptive statistics (chapters 2 through 4)
Visual methods such as charts and graphs
Numeric methods such as calculations

12

## Draw conclusions from the information

Inferential statistics (chapters 8 through 15)
Various methods that are appropriate for different questions and different types of data sets

13

## Characteristics of the individuals under study are called

variables
Examples of qualitative variables
Gender
Zip code
Blood type
States in the United States
Brands of televisions

14

## Qualitative variables have category values … those values cannot be

added, subtracted, etc.
Examples of quantitative variables
Temperature
Height and weight
Sales of a product
Number of children in a family
Points achieved playing a video game

15

## Quantitative variables have numeric values … those values can

be added, subtracted, etc.

16

## Discrete variables

Variables that have a finite or a countable number of possibilities
Frequently variables that are counts
The possible values of qualitative variables can be listed

17

## Continuous variables

Variables that have an infinite but not countable number of possibilities
Frequently variables that are measurements
Sometimes the variable is discrete but has so many close values that it could be considered continuous

18

## The process of statistics is designed to

collect and analyze data to reach conclusions

19

## Variables can be classified by their type of data

Qualitative or categorical variables
Discrete quantitative variables
Continuous quantitative variables

20

## There are different ways to collect data

Census
Existing sources
Survey sampling
Designed experiments
These are good methods of data collection, if done correctly

21

## A census is

a list.
Of all the individuals in a population
That records the characteristics of the individuals
An example is the US Census held every 10 years (this is only an example though)
Advantages
Answers have 100% certainty
Disadvantages
May be difficult or impossible to obtain
Costs may be prohibitive

22

## An existing source is

An appropriate data set has already been collected
That can be used for this study
Advantages
Saves time and money
Disadvantages
There may not be an applicable data set

23

## A survey sample is

A study when only a subset of the population is considered
A study where there is no attempt to influence the value of the variable of interest
Advantages
Saves time and money
Disadvantages
Choosing an appropriate sample could be difficult

24

## A survey sample is an example of an observational study

An observational study is one where there is no attempt to influence the value of the variable
An observational study is also called an ex post facto (after the fact) study
Advantages
It can detect associations between variables
Disadvantages
It cannot isolate causes to determine causation

25

## A designed experiment is an experiment

That applies a treatment to individuals
Often compares the treated group to a control (untreated) group
Where the variables can be controlled
Advantages
Can analyze individual factors
Disadvantages
Cannot be done when the variables cannot be controlled
Cannot apply in cases for moral / ethical reasons

26

## A simple random sample is

when every possible sample of size n out of a population of N has an equally likely chance of occurring
Examples
For a simple random sample of size n = 1 from a population size of N = 5, each of the 5 possible samples has an equally likely chance of occurring
For a simple random sample of size n = 2 from a population size of N = 4, each of the 6 p

27

## a frame

Simple random sampling requires that we have a list of all the individuals within a population
If we do not have a frame, then a different sampling method must be used

28

## There are other effective ways to collect data

Stratified sampling
Systematic sampling
Cluster sampling
Each of these is particularly appropriate in certain specific circumstances

29

## A stratified sample is

obtained when we choose a simple random sample from subgroups of a population
This is appropriate when the population is made up of nonoverlapping (distinct) groups called strata
Within each strata, the individuals are likely to have a common attribute
Between the stratas, the individuals are likely to have different common attributes

30

## A systematic sample is obtained

when we choose every kth individual in a population
The first individual selected corresponds to a random number between 1 and k
Systematic sampling is appropriate
When we do not have a frame
When we do not have a list of all the individuals in a population

31

## A cluster sample is obtained

when we choose a random set of groups and then select all individuals within those groups
We can obtain a sample of size 50 by choosing 10 groups of 5
Cluster sampling is appropriate when it is very time consuming or expensive to choose the individuals one at a time

32

## A convenience sample is obtained

when we choose individuals in an easy, or convenient way
Self-selecting samples are examples of convenience sampling
Individuals who respond to television or radio announcements
“Just asking around” is an example of convenience sampling
Individuals who are known to the pollster

33

## A multistage sample is obtained

using a combination of
Simple random sampling
Stratified sampling
Systematic sampling
Cluster sampling
Many large scale samples (the US census in noncensus years) use multistage sampling

34

## Sources of Error In Sampling

Poor design of the sampling frame
Poor design of the sample questions
One type of error, sampling errors, occur because we use only part of the population in our study
Samples consist of only part of the total data
Samples are usually more realistic to analyze
Because there are individuals in the population that are not in our sample, sampling erro

35

## Types of nonsampling error

Using an incomplete frame
Individuals who respond have different characteristics than individuals who do not respond
Interviewer errors
Misrepresented answers
Data checks
Questionnaire design
Wording of questions
Order of questions, words, and responses

36

## Interviewer errors may occur when

The interviewer has a vested interest in the results
The interviewer is not trained to obtain accurate information
The individuals feel pressure or an obligation to provide an answer that the interviewer desires
For example, if your server watches you when you fill out the restaurant’s service satisfaction questionnaire …

37

## A designed experiment is

is a controlled study
The purpose of designed experiments is to control as many factors as possible to isolate the effects of a particular factor
Designed experiments must be carefully set up to achieve their purposes

38

## explanatory variables

Some variables in a designed experiment are controlled, those are the
These variables are also sometimes called the factors
Factors
Are part of a controlled environment
Has values that can be changed by the researcher
Are considered as possible causes

39

## response variable

The designed experiment analyzes the affects of the factors on the

40

## A treatment

is a combination of the values of the factors
Examples of treatments
Giving one medication to one group of patients and a different medication to another
Using one type of fertilizer on a set of plots of corn and a different type of fertilizer on a different set of plots
Playing country music to one group of mice and rap music to another

41

## experimental units

(people, plants, materials, other objects, …)
When the experimental units are people, we refer to them as subjects
Subjects in an experiment correspond to individuals in a survey

42

## double-blind

When both the subjects and the researchers do not know which treatment, this is called

43

## placebo

Subjects not given any medication are often given a placebo such as a sugar tablet
The subjects will not know which treatment they get

44

## Conducting an experiment involves considerable planning

Planning steps
Identify the problem
Determine the factors
Determine the number of experimental units
Determine the level of each factor
Implementation steps
Conduct the experiment
Test the claim

45

## Three ways to deal with the factors

Control – fix the levels at a constant level (for factors not of interest)
Manipulate – set the levels at predetermined levels (for factors of interest)
Randomize – randomize the experimental units (for uncontrolled factors not of interest)

46

## replication

When a treatment is applied to more than one experimental unit

47

## A completely randomized design

design is when each experimental unit is assigned to a treatment completely at random
An example
A farmer wants to test the effects of a fertilizer
We choose a set of plants to receive the treatment
We randomly assign plants to receive different levels of fertilizer
This has similarities to completely random sampling

48

## A matched-pair design

is when the experimental units are paired up and each of the pair is assigned to a different treatment
A matched pair design requires
Units that are paired (twins, the same person before and after the treatment, …)
Only two levels of treatment (one for each of the pair)
An example
A subject before receiving the medication
The same subject after rec

49

## confounding

When two effects cannot be distinguished, this is called

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