3.12 K: Statistical Sampling

• What is systematic sampling?

  Every nth member is selected
• In cluster sampling, the population is divided into clusters
• Random sampling is used when the population is homogeneous and easy to access
• In which scenario is cluster sampling most useful?

  Geographically dispersed population
• Stratified random sampling divides the population into distinct subgroups called strata
• What is the first step in stratified random sampling?

  Divide population into strata
• Why is stratified sampling useful for heterogeneous populations?

  Reflects population diversity
• In cluster sampling, high sampling error can occur if clusters are not homogeneous
• Statistical sampling allows researchers to infer properties without examining every member of the population
• Proper statistical sampling is crucial for drawing accurate inferences about a population.

  True
• Simple random sampling ensures representation of all subgroups in the population.

  False
• Order the steps involved in stratified random sampling:

  1️⃣ Divide population into strata

  2️⃣ Determine stratum size

  3️⃣ Randomly select samples from each stratum
• Stratified sampling is suitable for populations with identifiable subgroups.

  True
• Stratified sampling ensures representation of important subgroups in the sample.

  True
• Match the sampling method with its advantage:

  Simple Random Sampling ↔️ Minimal bias

  Stratified Sampling ↔️ Ensures subgroup representation
• If clusters in cluster sampling are not homogeneous, the sampling error can be high.

  True
• Systematic sampling ensures even coverage of the population.
• Match the sampling method with its description:

  Random Sampling ↔️ Each member has equal chance

  Systematic Sampling ↔️ Every nth member is selected
• In random sampling, each member of the population has an equal chance of being selected
• Match the sampling method with its description:

  Random sampling ↔️ Each member has an equal chance

  Systematic sampling ↔️ Every nth member is selected

  Stratified sampling ↔️ Samples from subgroups

  Cluster sampling ↔️ Random sample of clusters
• Stratified sampling is used when the population is heterogeneous and contains identifiable subgroups.

  True
• Simple random sampling may not be representative if subgroups exist in the population.

  True
• Stratified sampling improves the precision of estimates by reflecting population diversity.


  True
• Stratified sampling can improve the precision of estimates compared to simple random sampling.


  True
• When is cluster sampling particularly useful?

  Geographically dispersed populations
• What is the purpose of statistical sampling?

  Estimate population characteristics
• What factors influence the choice of a statistical sampling method?

  Population heterogeneity and accessibility
• One advantage of simple random sampling is minimal bias
• What is a disadvantage of simple random sampling?

  Requires a complete population list
• Stratified sampling can improve the precision of estimates compared to simple random sampling
• What are the subgroups in stratified random sampling called?

  Strata
• When is stratified sampling most useful?

  When the population is heterogeneous
• What is a potential disadvantage of cluster sampling?

  High sampling error
• Steps involved in systematic sampling

  1️⃣ Determine the sampling interval (every nth member)

  2️⃣ Start randomly within the first interval

  3️⃣ Select every nth member thereafter
• What is a major disadvantage of systematic sampling?

  It is not truly random
• In what scenario is random sampling most suitable?

  Surveying customer satisfaction
• What is statistical sampling?

  Selecting a subset from a population
• Stratified sampling divides the population into subgroups and samples from each stratum.


  True
• What factors influence the choice of sampling method?

  Population heterogeneity, accessibility, goals
• When is systematic sampling most appropriate?

  When population is homogeneous