Sampling Error And Non–Sampling Error (Stats 165)

Introduction 

In statistical investigations, it is often impossible or impractical to study an entire population due to constraints of time, cost, and manpower. To overcome this, researchers select a sample, a small but representative portion of the population to draw conclusions about the whole. However, this process may lead to certain inaccuracies known as errors in sampling. These errors are generally classified into Sampling Errors and Non-Sampling Errors.

Types of Errors in Sampling

In a sample survey, two main types of errors may occur: Sampling Errors and Non-Sampling Errors.

1. Sampling Errors

Even though a sample represents a part of the population, it rarely gives exactly the same results as a complete enumeration (census).

The difference between a population parameter and its estimate obtained from the sample is called a Sampling Error.

Random sample error

These errors arise because only a portion of the population is observed, and can be reduced by increasing the sample size or by using better sampling techniques (such as stratified or systematic sampling).

        2. Non-Sampling Errors

These are errors not related to the act of sampling itself, but to mistakes in data collection, recording, processing, or interpretation.

They may occur in both sample surveys and censuses, and include:

Bias and Fairness 

• Response errors (when respondents give incorrect answers),
• Non-response errors, 
• Processing or tabulation mistakes, and
• Interviewer bias

Observer bias 

Advantages of Sampling

1. Saves time and labour: Only a portion of the population is studied.
2. Reduces cost: Requires less money and manpower than a census.
3. Wider scope: Allows for more intensive and detailed studies.
4. Quicker results: Enables faster decision-making.
5. More practical: Particularly useful when studying large or infinite populations.

Disadvantages of Sampling

1. Requires expertise: Sampling must be conducted by skilled and experienced personnel to ensure reliability.
2. Risk of Sampling Errors: Results may differ from true population values.
3. Possibility of bias: Poor sampling design can lead to unrepresentative samples.
4. Not suitable for small populations: Census methods may be preferable in such cases.

Conclusion

In summary, while sampling is a practical and cost-effective method of data collection, it is not entirely free from errors. Sampling errors arise due to studying only a part of the population, whereas non-sampling errors result from human or procedural mistakes during data handling. With proper planning, skilled personnel, and sound sampling techniques, these errors can be minimized, making sampling a reliable tool for accurate statistical investigation.

References 

U24PS Stats 165 note 

Stat 165 Key Points: By Rahmon Abass Adewale 

Wikipedia contributors