Random Sampling and Its Examples

What is stratified sampling?

• Stratified sampling involves selecting samples from only one subgroup of the population.

• Stratified sampling involves dividing the population into clusters and randomly selecting samples from each cluster.

• Stratified sampling involves selecting samples based on convenience rather than random selection.

• Stratified sampling involves dividing the population into subgroups and then randomly selecting samples from each subgroup.

Give an example of stratified sampling.

• Surveying teachers instead of students for feedback on the school's facilities.

• Surveying only 9th-grade students in a school for feedback on the school's facilities.

• Surveying students from different schools in the same grade for feedback on the school's facilities.

• Surveying students from different grades (9th, 10th, 11th, 12th) in a school to gather feedback on the school's facilities.

Explain cluster sampling.

• Cluster sampling involves selecting a random sample from the entire population without any grouping.

• Cluster sampling requires dividing the population into strata based on specific characteristics.

• Cluster sampling involves selecting only one element from each cluster in the population.

• Cluster sampling involves dividing the population into clusters, randomly selecting some clusters, and then including all elements within those selected clusters in the sample.

Provide a real-world scenario where cluster sampling can be used.

• Market research surveying customers in different geographical regions.

• Interviewing only individuals who agree to participate in a study to determine opinions

• Sampling every 10th person entering a store to understand shopping habits

• Conducting a survey at a single location to gather data on customer preferences

Define systematic sampling.

• Systematic sampling is a statistical method where elements are selected at regular intervals from an ordered list.

• Choosing every other element from a list

• Randomly selecting elements from a list

• Selecting elements based on their alphabetical order

How is systematic sampling different from simple random sampling?

• Systematic sampling selects elements alphabetically, while simple random sampling selects elements randomly.

• Systematic sampling selects elements based on their size, while simple random sampling selects elements randomly.

• Systematic sampling involves selecting every other element, while simple random sampling selects elements randomly.

• Systematic sampling selects elements based on a specific pattern, while simple random sampling selects elements randomly.

What is lottery random sampling?

• Lottery random sampling involves selecting a sample where only the outliers are included

• Lottery random sampling involves selecting a sample from a population where each member has an equal chance of being chosen.

• Lottery random sampling involves selecting a sample where the most common members are chosen

• Lottery random sampling involves selecting a sample based on alphabetical order

When would lottery random sampling be an appropriate method to use?

• When the population is small and easily accessible.

• When the population is large and it is not feasible to reach every individual.

• When the researcher wants a representative sample.

• When the study requires in-depth interviews with participants.

Describe multistage sampling.

• Multistage sampling requires selecting samples from a single cluster or stratum.

• Multistage sampling involves selecting samples from the entire population at once.

• Multistage sampling involves dividing the population into clusters or strata, then selecting samples from each cluster or stratum in subsequent stages.

• Multistage sampling means selecting samples from different populations in each stage.

In what situations would multistage sampling be advantageous?

• Multistage sampling is advantageous when the researcher has prior knowledge of the entire population.

• Multistage sampling is advantageous in situations where the population is large and geographically dispersed.

• Multistage sampling is advantageous in situations where the population is small and concentrated.

• Multistage sampling is advantageous when the data collection process needs to be quick and efficient.