Sampling Methods and Bias: Learn It 3

Sampling Methods Summary

Let’s sum up the sampling methods so far.

sampling methods

Simple random sampling assigns a number to every member of the population, then uses a random number generator to select a sample.

Systematic sampling assigns a number to every member of the population, then choses individuals or entities from the population at regular intervals (e.g. every 4th individual from a randomly selected starting point).

Stratified sampling divides a population into groups via some criterion, then uses simple random selection or systematic selection to collect a sample from each group.

Cluster sampling divides a population into groups via some criterion, then uses simple random selection or systematic selection to select one or more groups as the sample.

Convenience sampling selects a sample most accessible to the researcher.

A sampling method is unbiased if, on average, it results in a representative sample of the population. A sampling method is biased if it has a tendency to produce samples that are not representative of the population. If the sampling method is biased, we cannot generalize our results to the population and can only make statements about the sample itself.

Choose Your Own Dataset

For this problem, you'll create and analyze different samples using a data set of your choosing.