Sampling Methods

The best samples to represent a population is the one that despite being a subset of the population, the sample still accurately represents the population.

Importance of a Good Sample

Minimizing Bias

When selection is not random, certain segments of the population may be over-represented or under-represented.

This introduces sampling bias, leading to inaccurate conclusions about the population.

Generalizability

A representative sample allows researchers to generalize findings from the sample back to the larger population with a measurable degree of confidence. Without representativeness, the findings are only applicable to the sampled group.

Sampling Techniques

Technique	How it works	Example
Simple Random	Choose a specific number of people from the entire population randomly with equal probability	Putting the names of all the students in a class and drawing 10 people
Systematic	Sort the population and choose people at regular intervals	All the students in a school sorted alphabetically and choose a starting point then pick at regular intervals
Stratified	Divide the sample into meaningful groups, then select from each group based on proportions	1000 participants, 633 identify as women, 367 identify as men. The sample now consist of 63 to 37 (identity of women to men)
Cluster	divide the population into groups, randomly choose a number of the groups, sample from each group chosen	From YRDSB, 10 schools were selected. All grade 9 students are asked to complete a survey.
Convenience	Sample is chosen based on ease of access (problematic and can create unreliable conclusions)	A school interviews the first 10 students they see before school starts for a student climate survey
Voluntary	Data is only collected from people who participate (problematic as only the people who volunteer describe the population)	Voting for a new premier or prime minister in Canada