What is random sampling?

Random sampling is a method of selecting or assigning data points by chance rather than choosing them according to a fixed pattern.

In machine learning, it is commonly used when creating training, validation, and test datasets from independent observations.

Simple example

Suppose a dataset contains 10,000 customer records.

Instead of taking the first 7,000 records for training and the last 3,000 for evaluation, the records can be shuffled and assigned randomly to different splits.

Original dataset → Shuffle randomly → Training / Validation / Test

This helps prevent the original ordering of the data from creating unintended bias in the split.

Why random sampling is useful

Random sampling can help:

• give each observation a fair chance of appearing in a particular split
• reduce bias caused by the original order of the dataset
• create subsets that are more representative of the overall data
• support a fairer comparison between training and evaluation performance

When random sampling works well

It is most appropriate when individual observations are reasonably independent and the dataset does not have an important ordering or grouping structure.

For example, randomly splitting a dataset of independent customer records may be reasonable when each customer appears only once.

When simple random sampling may not be enough

Random sampling should be used carefully when:

• classes are highly imbalanced
• multiple rows belong to the same person, account, device, or other group
• the order of observations matters
• the data represents events over time

In those cases, methods such as stratified, grouped, or time-based splitting may be more appropriate.

In short, random sampling selects or assigns observations by chance so machine learning datasets are not split according to an arbitrary original ordering.