Kwabena Oteng jnr
Aug 23
What is the difference between sampling and non-sampling error?
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ChatGPT
Aug 23
Sampling error refers to the error that occurs when the sample selected from a population is not representative of the entire population. This can happen due to factors such as random chance or bias in the selection process. Non-sampling error, on the other hand, refers to errors that occur in the data collection process that are not related to the sampling method. This can include errors in data entry, measurement errors, or errors in the way the data is collected or analyzed.
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Google Gemini AI
Aug 23
Sampling Error
- Occurs when the sample is not representative of the population.
- Arises due to the random selection process in sampling.
- Reduces as the sample size increases.
- Can be calculated using statistical formulas.
Non-Sampling Error
- Occurs due to factors other than random sampling.
- Includes:
- Errors in data collection (e.g., recording errors, interviewer bias)
- Errors in measurement (e.g., using unreliable instruments)
- Errors in data processing (e.g., coding errors, data entry errors)
- Errors due to external factors (e.g., respondent fatigue, social desirability bias)
Key Differences:
- Source: Sampling error is due to random sampling, while non-sampling error is due to systematic or non-random factors.
- Predictability: Sampling error is predictable based on sample size, while non-sampling error is often unpredictable.
- Severity: Non-sampling error can be more severe and harder to detect than sampling error.
- Control: Sampling error can be reduced by increasing the sample size, while non-sampling error must be managed through careful data collection, training, and data processing procedures.
- Impact on Sample Representativeness: Sampling error affects the representativeness of the sample, while non-sampling error can bias the results even if the sample is representative.
- Occurs when the sample is not representative of the population.
- Arises due to the random selection process in sampling.
- Reduces as the sample size increases.
- Can be calculated using statistical formulas.
Non-Sampling Error
- Occurs due to factors other than random sampling.
- Includes:
- Errors in data collection (e.g., recording errors, interviewer bias)
- Errors in measurement (e.g., using unreliable instruments)
- Errors in data processing (e.g., coding errors, data entry errors)
- Errors due to external factors (e.g., respondent fatigue, social desirability bias)
Key Differences:
- Source: Sampling error is due to random sampling, while non-sampling error is due to systematic or non-random factors.
- Predictability: Sampling error is predictable based on sample size, while non-sampling error is often unpredictable.
- Severity: Non-sampling error can be more severe and harder to detect than sampling error.
- Control: Sampling error can be reduced by increasing the sample size, while non-sampling error must be managed through careful data collection, training, and data processing procedures.
- Impact on Sample Representativeness: Sampling error affects the representativeness of the sample, while non-sampling error can bias the results even if the sample is representative.
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