Question

A data analyst is developing a model. They start by gathering data for groups that are underrepresented in a sample. What strategy could they employ to ensure these groups are represented fairly?

1) Random sampling
2) Stratified sampling
3) Cluster sampling
4) Convenience sampling

Answer 1

To fairly represent underrepresented groups in a sample, the data analyst could use stratified sampling, which ensures each subgroup is proportionately included in the sample.

Step-by-step explanation:

To ensure that underrepresented groups are represented fairly in a model, a data analyst could employ stratified sampling. This method involves dividing the population into groups or strata based on certain characteristics, and then selecting a proportional sample from each stratum using simple random sampling. This approach ensures that each subgroup of the population is adequately represented in the sample, which can provide more reliable and valid results than other sampling methods, such as convenience sampling, which is often biased.

For example, if a researcher wants to ensure fair representation of different age groups in a study, they could divide the population into separate age strata and then randomly select individuals from each stratum. This is in contrast to convenience sampling, which involves selecting individuals who are conveniently available and may not represent the population accurately.

Other sampling methods like cluster sampling and systematic sampling also use random selection, but they may not guarantee that all subgroups are represented if not specifically designed to do so.