Transcript 2.4 Sampling - Glacier Peak High School

2.4 Sampling
 To Get a perfect set of data, we would survey every person in
the population
 Census: Obtaining information from an entire population
 Difficult to do…
 Limited Resources
 Process is destructive and would be foolish
 Sample should be representative of the population
Bias in Sampling
 Bias: Systematically leading the researcher to an outcome
 Selection Bias
 Measurement Bias
 Response Bias
 Non-response Bias
Selection Bias
Undercoverage
 When some part of the population is systematically excluded
 Telephone surveys exclude people without telephones or those
people who aren’t at home in the evenings, etc.
 Self-Selected (volunteers): Only those with an interest in the
topic complete the survey (like calling in to radio station, etc.)
Measurement Bias
 Data don’t represent the true population due to some sort of
measurement error
Response Bias
 Produces values that systematically differ from the true
population in some way…
 The way questions are worded on a survey
 Appearance or Behavior of the person asking the question
 The group or organization conducting the study
 People have a tendency to lie when asked about illegal behavior
or unpopular beliefs
Non-Response Bias
 Occurs when responses are not obtained from everyone in
the sample
 Those without an opinion either way don’t return the survey
 Mail Surveys are least expensive, but have the worst response
rates
 Telephone surveys are more costly but have a better response
rate
 Personal Interviews are very expensive, but have the best
response rate
Random Sampling
 Simple Random Sampling (SRS)
 Systematic Sampling
 Sampling with replacement
 Sampling without replacement
 Stratified Random Sampling
 Multi-Stage Sampling
 Cluster Sampling
 Convenience Sampling
Sample Size
 Represented by n
 The best way to solve all issues in AP Stats…
 Increase the Sample Size
Simple Random Sampling
 Every person in the population has an equal chance of being
drawn.
 Best Way: “Put them in a hat, shake them up, draw them out”
 Drawbacks: mixing must be adequate and process can be
tedious
 Sampling Frame: Create a List of all objects/individuals in
the population
 Use a Random number generator or digitable to select the
sample
Random Sampling
 It is possible for sampling to be random, without being
SRS…
 Selecting 64 NFL Football Players
 Random Sampling does not guarantee that the sample will be
representative…
 We have to rely on our methods being adequate choices for the
sample
Systematic Sampling
 Random– but there is a well-defined pattern to the selection
 Randomly select one of the first 10 names in the phone book,
then select every 10th name after that to be in the sample.
Sampling
 With Replacement…
 Put Names/Numbers back into the hat
 Allows for the possibility for an item or individual to appear more than
once in the sample
 Rarely Used in practice
 Without Replacement…
 Don’t put the names/numbers back into the hat
 Used More often
 When the sample size is small relative to the population size
(which is typical), there is little practical difference between
replacement and without replacement
Stratified Random Sampling
 Used when the entire population can be divided into a set of
non-overlapping groups (strata)
 Used when it is important to obtain information about
characteristics of the individual strata
 Can produce more accurate results because each strata may
be more homogeneous than the entire population
Cluster Sampling
 Sampling pre-existing groups
 Census the Entire Population of the Cluster
 Cluster vs. Stratified Sampling
 Makes life easier by breaking it down
Multi-Stage Sampling
 Sampling that combine several methods
 Breaks down the groups – makes life a little easier
Convenience Sampling
 Obtaining a Sample any way you can
 Easy for the researcher
 Lots of bias!
 Avoid This Technique!