Sampling
Sampling is the process of selecting a subset of observations or data points from a larger population or dataset, to gather information about it in a more efficient and cost-effective manner, as it is often impractical or impossible to collect data from the entire population.
It is important to consider potential sources of bias and implement appropriate sampling strategies to minimize bias and ensure the validity of the findings.
Interview questions on sampling as a standalone topic are less frequently asked. However, sampling is a foundational technique applied to bigger topics such as hypothesis tests and A/B tests, so we recommend reviewing the below example questions to assess your understanding.
What to expect
Example questions include:
- What are the advantages and disadvantages of random sampling?
- What’s the difference between analyzing data from the population vs. samples from the population?
- When sampling data for analysis, what would you consider to verify that the samples are good?
- How do you evaluate if samples are biased?
This lesson will explain common types of sampling and provide examples for each.
Simple random sampling
Every member of the population has an equal chance of being selected for the sample. Random sampling ensures that the sample is representative of the population and reduces bias in the estimation of population parameters.
A researcher selects 100 students from a school by assigning each student a unique number and using a random number generator to select the sample.
Systematic sampling
In systematic sampling, individuals or observations are selected at regular intervals from a list or ordered population. It is similar to random sampling but slightly easier to implement.
A quality control manager selects every 10th item from a production line for inspection.
Stratified sampling
The population is divided into distinct subgroups or strata based on certain characteristics (e.g. age, gender, location). Samples are then independently selected from each stratum, ensuring representation from all subgroups in the population.
Stratified sampling is useful when certain subgroups are of particular interest or when there is variability within the population.
A market researcher divides customers into age groups (e.g. 18-25, 26-35, 36-45) and then randomly selects individuals from each age group for a survey.
Cluster sampling
The population is divided into clusters or groups, and a random sample of clusters is selected. All members of the selected clusters are included in the sample.
Cluster sampling is useful when it is impractical to sample individuals directly and when clusters are representative of the population.
A researcher selects several classrooms from different schools and surveys all students in the selected classrooms.