Sampling Error

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.