1. Agenda
Vocabulary review
Sampling review (Section 1.3)
Homework review (Section 1.3)
Sample test question 1.3

2. Stratified Random Sampling (With proportional allocation the different strata have same sampling fractions.)
Examples
The Low-income, middle-income, upper-income Sample of 20 homeowners for a town swimming pool.
Freshmen, Sophomore, Junior, Senior sample of 24 dormitory residents
Uses
To highlight a specific subgroup
To observe relationships between two or more groups
So the researcher can representatively sample even the smallest and most inaccessible subgroups in the population (Like a low-income group in the swimming pool situation)
- With this technique, you have a higher statistical precision compared to simple random sampling. This is because the variability within the subgroups is lower compared to the variations when dealing with the entire population. Because this technique has high statistical precision, it also means that it requires a small sample size which can save a lot of time, money and effort of the researchers.

3. Cluster Sampling Examples:
Bike path survey of 18,000 homes divided into 947 blocks (15 random blocks were then chosen to interview all 20 homes)
Uses:
This sampling technique is cheap, quick and easy. Instead of sampling an entire country when using simple random sampling, the researcher can allocate his limited resources to the few randomly selected clusters or areas when using cluster samples.
Disadvantages:
From all the different type of probability sampling, this technique is the least representative of the population. The tendency of individuals within a cluster is to have similar characteristics and with a cluster sample, there is a chance that the researcher can have an overrepresented or underrepresented cluster which can skew the results of the study.

4. Difference Between Cluster Sampling and Stratified Sampling
The main difference between cluster sampling and stratified sampling lies with the inclusion of the cluster or strata.
In stratified random sampling: all the strata of the population is sampled
In cluster sampling: the researcher only randomly selects a number of clusters from the collection of clusters of the entire population. Therefore, only a number of clusters are sampled, all the other clusters are left unrepresented.

5. Systematic Sampling Examples: College algebra attitude survey
International 500 firms sample
20 ball sample of 80 ball population in Keno
Uses:
Simple and It allows the researcher to add a degree of system or process into the random selection of subjects.
Population is evenly sampled
Disadvantages:
Like in the international 500 example, the researcher must be careful to make sure that the chosen interval between subjects in the sample does not reflect a certain pattern of traits present in the population. If a pattern in the population exists and it coincides with the interval set by the researcher, randomness of the sampling technique is compromised.