Lesson 3 of 8 · All lessons in this module
- Populations, samples and sampling error
- Stratified and cluster sampling
- Convenience and judgmental sampling, and choosing a method
- Sample size and mixing populations
- The central limit theorem
- The standard error of the sample meanVideo lesson
- Confidence intervals for the mean
- Bootstrap and jackknife resampling
Lesson 3 of 8 · 12 min
Convenience and judgmental sampling, and choosing a method
Non-probability samples are chosen by convenience or expert judgment: quick and cheap, but at real risk of not representing the population.
In short
- Non-probability methods rely on the researcher's selection, not on a fixed random process, so not every member has an equal chance of being chosen.
- Convenience sampling: use what is easiest to access. Fast and low cost, ideal for a pilot study, but the sample may not represent the population, so accuracy can be limited.
- Judgmental sampling: an expert handpicks members. Useful under time pressure or when expertise is critical (e.g. auditing), but exposed to the researcher's bias.
- Probability methods let chance alone drive sampling error; non-probability methods add the risk of a systematically unrepresentative sample.
- Match the method to the goal: inference about a population → probability sampling; index replication → stratified; vast population on a budget → cluster; quick first look → convenience.
Unlock this lesson free for 7 days
Create a free account to get 7 days free: every lesson, video and flashcard, and the question bank. No card needed.