Distinguish sampling error from non sampling error.
The correct answer and explanation is :
Distinguishing Sampling Error from Non-Sampling Error
Sampling Error:
A sampling error occurs due to the natural variation that arises when a sample, rather than the entire population, is used to estimate characteristics of the population. It results from the fact that no sample can perfectly represent the entire population. This error decreases as the sample size increases and can be minimized using proper sampling techniques, such as random sampling.
Non-Sampling Error:
Non-sampling errors, on the other hand, arise from factors unrelated to the sample selection process. These errors occur due to mistakes in data collection, processing, response bias, measurement errors, or even survey design flaws. Unlike sampling errors, non-sampling errors cannot be reduced simply by increasing the sample size.
Explanation (300 Words)
Sampling error and non-sampling error are both common issues in statistical studies, but they differ in their causes and solutions.
A sampling error occurs because a sample is only a subset of a population, and there is always a chance that it does not perfectly represent the entire group. For example, if a researcher selects 1,000 people from a city of 1 million, the sample may not capture all population characteristics, leading to discrepancies between sample estimates and true population values. This type of error can be reduced by increasing the sample size or using techniques like stratified sampling.
In contrast, non-sampling errors stem from factors other than sample selection. These errors may arise due to inaccurate data collection, biased questionnaire wording, interviewer influence, non-responses, or processing mistakes. For example, if respondents misinterpret survey questions or provide false answers, it introduces inaccuracies that have nothing to do with sample selection. Non-sampling errors can be much more damaging than sampling errors because they can distort results in unpredictable ways.
To minimize errors, researchers must design unbiased surveys, train interviewers properly, and ensure accurate data processing. While increasing the sample size can reduce sampling error, it does not eliminate non-sampling errors, which require careful data collection and analysis techniques.
