Sampling error is the error that results from selecting a sample that does not accurately represent the population from which it was drawn.
Causes of Sampling Error:
- Selection bias: The way in which samples are selected can lead to biased results, as certain individuals or groups may be more likely to be selected than others.
- Sample size: A small sample size can increase the likelihood of sampling error.
- Random variation: The randomness of sample selection can cause the sample to differ from the population in unexpected ways.
- Non-response: Some individuals in the population may not respond to the survey, which can introduce bias.
- Data collection errors: Errors during data collection or processing can introduce sampling error.
Examples of Sampling Error:
- A survey of college students that overestimates the percentage of students who are satisfied with their education.
- A poll that underestimates the number of people who support a particular candidate.
- A sample of bacteria that does not accurately represent the entire population of bacteria.
Measures to Reduce Sampling Error:
- Random selection: Using random sampling methods to select individuals for the sample.
- Large sample size: Selecting a large sample size to reduce the impact of random variation.
- Elimination of bias: Taking steps to minimize selection bias, such as using stratified sampling methods or balancing the sample to match the known population proportions.
- Non-response follow-up: Reaching out to non-respondents to ensure their voices are included.
- Data validation: Checking for data collection errors and using data validation techniques to ensure accuracy.
Key Points:
- Sampling error is the error that results from selecting a sample that does not accurately represent the population.
- Causes of sampling error include selection bias, sample size, random variation, non-response, and data collection errors.
- Measures to reduce sampling error include random selection, large sample size, elimination of bias, non-response follow-up, and data validation.
Updated on February 06, 2023
