Sampling Methods

Getting a sample wrong can quietly produce wrong conclusions — whether you just grab whoever's easiest to reach, or assume any "random-looking" group of people is good enough. This demo shows why: a population of 60 people from three countries is shown, each with their own average daily tea consumption. Draw a sample using a convenience (biased) pick, simple random sampling, or stratified random sampling, and compare it to the true population. Stratified sampling is one method built to guard against this kind of error — several others exist too, each suited to different situations, but aren't covered here.

Each dot is a person, coloured by country, and carries a hidden value — cups of tea they drink per day. Choose a sampling method, draw a sample, and see how its country mix and average cups per day compare to the full population below.

What this teaches: Draw samples from a population of 60 people using convenience, simple random and stratified sampling, and compare each sample's estimate with the true population average. It teaches sampling bias, sampling variance and how stratification can reduce error.

Sampling Methods ties directly into the Data Collection and Data Management module of DAVHILL’s Intro to Data Science (opens in a new tab) course.