stratified sampling tweets

Stratified sampling is a sampling technique that involves dividing the data into different strata (groups) based on certain characteristics, and then selecting a random sample from each stratum. This can be useful when the goal is to ensure that the sample is representative of the population, or when certain subgroups need to be oversampled or undersampled.

With stratified sampling, you divide the population into groups. Then, you sample from them in direct proportion to the group size.

For example, you create two groups. The first contains valid transactions, and the second contains fraudulent ones. You then sample from both groups proportionally to their size.

Stratified sampling always works well unless you can't divide samples into groups.

Pros:

• Ensures that the sample is representative of the population
• Allows for oversampling or undersampling of specific subgroups

Cons:

• Requires a good understanding of the data and the characteristics that are important for stratification
• May not work well for small datasets