2.1 Statistical Sampling

• Statistical sampling is the process of selecting a subset of a larger population to represent and make inferences about the entire population
• Statistical sampling reduces costs and time compared to surveying the whole population.
• What do market researchers use statistical sampling to understand?

  Customer preferences
• The population is the entire group being studied, while a sample is a subset
• Match the characteristic with the correct group:

  Complete size ↔️ Population

  Subset size ↔️ Sample
• Stratified sampling divides the population into subgroups and takes random samples from each stratum
• What is the primary advantage of cluster sampling?

  Cost reduction
• Random sampling ensures that each member of a population has an equal chance of being selected.
• Order the following sampling methods from most to least biased:

  1️⃣ Cluster Sampling

  2️⃣ Systematic Sampling

  3️⃣ Stratified Sampling

  4️⃣ Simple Random Sampling
• What is the key characteristic of simple random sampling?

  Equal chance of selection
• In stratified sampling, the population is divided into strata
• Cluster sampling involves dividing the population into subgroups and sampling all individuals within selected clusters.
• What is the primary goal of random sampling techniques?

  Minimize bias
• Stratified sampling divides the population into subgroups called strata
• In cluster sampling, what is randomly selected from the population?

  Clusters
• Simple random sampling ensures every individual has an equal chance of selection.
• Match the sampling method with its description:

  Simple Random Sampling ↔️ Equal chance of selection

  Stratified Sampling ↔️ Divided into subgroups

  Cluster Sampling ↔️ Divided into clusters
• Order the steps to understand student preferences using stratified sampling:

  1️⃣ Divide students by faculty (strata)

  2️⃣ Randomly sample 100 students from each faculty

  3️⃣ Poll the selected students

  4️⃣ Analyze the data
• Stratified sampling ensures representation of all strata
• Stratified sampling is suitable for understanding income across different age groups.
• Systematic sampling involves selecting every nth element from a population list.
• How is the sampling interval $k$ calculated in systematic sampling?


  $k =$$\frac{N}{n}$
• Systematic sampling is simpler to implement than simple random sampling.
• What is the primary purpose of statistical sampling?

  Make inferences about population
• A sample is a subset of the entire population being studied.
• The population is always larger in size compared to the sample.
• What is the primary advantage of random sampling techniques?

  Eliminates bias
• In systematic sampling, every nth individual is selected from the list.
• What are three advantages of stratified sampling?

  Representativeness, reduced error, detailed information
• Stratified sampling is suitable for understanding average income across different age groups.
• Systematic sampling involves selecting every nth element from a population list.
• How is the sampling interval k</latex> calculated in systematic sampling?

  $k =$$\frac{N}{n}$
• In cluster sampling, what is the primary unit of sampling?

  Clusters
• One advantage of cluster sampling is its cost-effectiveness for geographically spread populations.
• Cluster sampling simplifies logistics compared to other methods.
• If you need to sample 10% of 500 employees using systematic sampling, the sampling interval would be 10
• Systematic sampling ensures even coverage across the population.
• In cluster sampling, the population is divided into clusters
• Cluster sampling is cost-effective for large populations spread geographically.
• Non-random sampling methods have a higher risk of bias