This paper provides five cases that explain population, samples and probability sampling techniques.
The situations in which these sampling techniques should be used are summarized in the table below:. Download White Paper on Sampling Techniques 1.
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Then we discuss the following sampling techniques which are categorized as Probability sampling: Simple random sampling Stratified sampling Systematic sampling Cluster sampling single stage and two stage. The situations in which these sampling techniques should be used are summarized in the table below: Sampling Technique Situation to use in Simple Random High risk of data manipulation Speed of sampling more important than precision Stratified Population needs to be divided into homogenous subpopulations to identify the specific problem area When the population shows periodicity Systematic Population does not show any patterns Low risk of data manipulation Cluster When dealing with a very large population where a large sample size is required relative to population and not all members of the population are equally accessible.
Compare to Sample Checking Account Activity which analyzes activity of members with checking account activity in the prior month. Discuss the power of sampling member data to find the best and the worst actors in your membership base.
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