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Sampling Bias
A good sample is unbiased, meaning that the sample has the same attributes as the population of interest.
In other words, if a sample does not accurately represent the population of interest, then the sample is biased.
Different types of sampling biases
Non-random sampling bias
- The sample must be randomly extracted from the population - You cannot cherry-pick!
- You should not perform convenience sampling!
Voluntary response bias
- Members decide if they if want to be part of the sample or not.
- This biased sample will not be representative of the whole population.
- Example People who are bored or have very strong opinions about a particular subject are more willing to participate (i.e. self-selection). You will end up with a biased sample full of bored and very opinionated people.
Nonresponse bias
- If your sampling method is prone to low response (e.g. mailing surveys) as individuals that are chosen may be unable or unwilling to participate.
Undercoverage bias
- This happens when a specific type of individuals in a population is not adequately represented in the sample.
- Example Females make up only 29% of engineering majors at UCLA. If you only survey students near the men’s washroom at the Engineering Student Centre (i.e. convenience sampling), then females may likely be unrepresented in your sample.
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Recipe for a Good Sample
When determining the best sampling method, we need to consider the sample size, randomization of the sample, and potential sampling biases.
Size
- A sample needs to be large enough according to the Central Limit Theorem but it does not need to be humungous.
- There is no magic fraction of the population that we need to sample, but generally we do not sample over 10% of the population.
- Additionally, we have no control over the population size.
Randomization
- Obtaining a random sample is key.
- Randomly selecting subjects in a sample minimizes the chance of obtaining a biased and non-representative sample.
Bias
- Your sample needs to be free from bias.
- A biased sample is not representative of the population of interest.
Practice: Sampling Bias
Which of the following could reduce sampling bias?
Practice: Good Sampling
A study on wellbeing and stress of university students is conducted. The researcher drew a random sample of 5 departments on campus. Then he randomly sampled 100 students within each department that was selected and handed them the link to his online survey. Ultimately, only 30% of the sample responded. Amongst those who responded, 120 of them say they feel stressed.
Which of the following is definitely true?