Sampling Error

TL;DR

• Sampling error appears when a sample does not perfectly reflect the population, reducing accuracy and reliability.
• Common causes are non-random selection and insufficient sample size.
• Typical mitigations include random sampling, increasing sample size, and stratified sampling.

Definition

Sampling error refers to the difference between the characteristics of a sample and the characteristics of the population from which it is drawn. It is a natural occurrence in any sampling process and can impact the accuracy and reliability of the results obtained from the sample.

Explanation

Sampling error arises because a sample, even when collected correctly, may not exactly match the population’s characteristics. It can result from how the sample is selected (for example, non-random procedures) or from the sample’s size (for example, being too small). Because sampling error affects how well sample-based results generalize to the full population, it can reduce both accuracy and reliability of inferences made from the sample.

Examples

Non-random sampling

This occurs when the sample is not selected randomly from the population but instead is based on specific criteria or biases. For example, a study on the attitudes of college students towards climate change may select a sample of students from a single university rather than sampling from a diverse group of colleges. The attitudes of students at one university may not represent college students more broadly, producing sampling error.

Small sample size

If the sample size is too small, it may not accurately represent the population and lead to sampling error. For example, a study on the effectiveness of a new medication may be conducted on a small sample of 50 participants. This sample may not be representative of the entire population, and results could differ if the study were conducted on a larger sample.

Notes or pitfalls

• Sampling error is a natural part of sampling and can reduce accuracy and reliability of results.
• Non-random sampling introduces bias by making some population members less likely to be included.
• Small sample sizes increase the likelihood that the sample will not represent the population.
• Common ways to minimize sampling error:
  • Random sampling: give all population members an equal chance of selection to reduce bias.
  • Increase sample size: larger samples are more likely to reflect the population.
  • Stratified sampling: divide the population into strata and sample each stratum to improve representativeness.

Related terms

• Random sampling
• Non-random sampling
• Stratified sampling
• Sample size
• Sample
• Population