Short Answer
Sampling errors are the errors that occur when only a part of the population is selected instead of the whole population. These errors happen because the sample may not perfectly represent the population.
Non-sampling errors are errors that occur due to mistakes in data collection, recording, or processing. These can happen in both census and sample surveys. They include human errors, bias, and incorrect data handling.
Detailed Explanation:
Sampling and Non-Sampling Errors
Sampling Errors
Sampling errors arise when data is collected from a sample instead of the entire population. Since only a small part is studied, there is always a chance that the sample does not fully represent the population.
These errors occur due to the difference between the sample result and the actual population value. For example, if a sample is not properly selected, it may give a wrong idea about the whole population.
Causes of Sampling Errors
- Improper selection of sample
- Small sample size
- Lack of proper sampling techniques
- Bias in choosing sample units
Features of Sampling Errors
- Occur only in sample surveys
- Can be measured and estimated
- Can be reduced by increasing sample size and using proper methods
Ways to Reduce Sampling Errors
- Use proper sampling techniques
- Select a large and representative sample
- Avoid bias while selecting sample units
- Use random sampling methods
Non-Sampling Errors
Non-sampling errors are errors that occur during data collection, processing, or analysis. These errors can happen in both census and sample surveys. They are not related to the size of the sample.
These errors are usually caused by human mistakes or poor data handling. They can affect the accuracy of the data more seriously than sampling errors.
Causes of Non-Sampling Errors
- Mistakes in recording data
- Wrong responses by respondents
- Poor questionnaire design
- Lack of training of investigators
- Data processing errors
Features of Non-Sampling Errors
- Occur in both census and sample surveys
- Difficult to measure and control
- Can lead to serious inaccuracies
Ways to Reduce Non-Sampling Errors
- Proper training of data collectors
- Clear and simple questionnaire design
- Careful data recording and checking
- Use of reliable data processing methods
Difference between Sampling and Non-Sampling Errors
Nature of Errors
Sampling errors are due to studying only a part of the population. Non-sampling errors are due to mistakes in the process of data collection and handling.
Occurrence
Sampling errors occur only in sample surveys. Non-sampling errors occur in both census and sample methods.
Control and Measurement
Sampling errors can be measured and reduced. Non-sampling errors are difficult to measure and control.
Impact on Data
Sampling errors affect the representativeness of the sample. Non-sampling errors affect the accuracy and reliability of the data.
Conclusion
Sampling errors and non-sampling errors are two important types of errors in statistics. Sampling errors occur due to the use of a sample, while non-sampling errors occur due to mistakes in data collection and processing. Both types of errors affect the quality of data, so proper methods and care are needed to reduce them.