Transcript Slide 1

Probability & Statistics
Sampling Techniques
Sampling Techniques
13.1 Sampling Techniques
Population: All items or people of interest.
Sample: Some items of a population.
Sampling Techniques
Statisticians often use a sample in order to make
predictions about a whole population.
When a statistician draws a conclusion from a
sample, there is always the possibility that the
conclusion is incorrect.
Sampling Techniques
Why use a sample instead of a population?
It is often impossible to obtain data on
an entire population.
Sampling is less expensive because collecting
data takes less time and effort.
More simply put…
Studying samples saves
Sampling Techniques
In this unit, we will study some sampling techniques
that attempt to collect unbiased samples of different
An unbiased sample is a small replica of the
entire population.
1. having no bias or prejudice; fair or impartial
2. statistics (of a sample) not affected by any
extraneous factors, conflated variables, or selectivity
which influence its distribution; random
Sampling Techniques
If care isn't taken while collecting data or selecting
a sample of the population, results could be
Bias: a particular tendency or inclination, especially one that
prevents unprejudiced consideration of a question;
Sampling Techniques
Sampling Techniques
Random Sampling
A sample drawn so that each item in the population
has an equal chance of being selected.
For example, a jar contains 200 marbles that are identical
except for color. Half of the marbles are red and the other
half of the marbles are blue. A random sample can be
easily achieved by selecting marbles from the jar (without
Sampling Techniques
Systematic Sampling
A sample obtained by drawing every nth item on a
list or production line. (The first item should be
determined by using a random number.)
For example, to determine how often damaged items are
produced, every tenth item is checked for damage.
(In this example, n = 10.)
Sampling Techniques
Cluster Sampling (or Area Sample)
A random selection of groups or units. (Groups are
often geographic locations.)
For example, to estimate the average income of the
residents of Lynn, a random sample of 100 residents was
taken from each zip code in the city.
Sampling Techniques
Stratified Sampling
Dividing the population by characteristics that can
usually be ordered by level. (Income, career, and
social status are typical stratified characteristics, but
gender, race, or religion can also be considered
For example, upper income, middle income, and lower
income families were sampled to determine the average
number of children per household.
Sampling Techniques
Convenience Sampling
Data that is easily or readily obtained.
(Warning: This type of sampling can be extremely biased)
For example, a teacher was interested in predicting who
would become the senior prom queen. His period two
class consisted of all seniors. He surveyed the period
two class to see who they would vote for at the prom.
Sampling Techniques
Review: 5 Sampling Techniques
Random Sampling: Blindly choosing from a whole population
Systematic Sampling: Choosing every nth item
Cluster Sampling: Taking a sample from different Areas or groups
Stratified Sampling: Taking samples from different levels
Convenience Sampling: Sampling whatever is easy
Sampling Techniques
Try this.
Identify the sampling technique described.
A raffle ticket is drawn by a blindfolded person
at a festival to win a grand prize.
Students at an elementary school are classified
according to their present grade level. Then,
a random sample of three students from each
grade is chosen to represent their class.
Every sixth car on highway is stopped for a
vehicle inspection.
Sampling Techniques
Try this.
Voters are classified based on their polling location.
A random sample of four polling locations is
selected. All the voters from the precinct are
included in the sample.
The first 20 people entering a water park are asked
if they are wearing sunscreen.