Sampling and Non Sampling Errors

Before differentiating the Sampling and Non Sampling Errors, let us define the Error term first.

The difference between an estimated value and the population’s true value is called an error. Since a sample estimate is used to describe a characteristic of a population. A sample being only a part of the population cannot provide a perfect representation of the population, no matter how carefully the sample is selected. Generally, it is seen that an estimate is rarely equal to the true value, and we may think about how close the sample estimate is to the population’s true value.

Two Kinds of Errors: Sampling and Non Sampling Errors

There are two kinds of errors, namely (I) Sampling Errors and (II) Non Sampling Errors

1. Sampling Errors (random error)
2. Non-Sampling Errors (non-random errors)

Sampling Errors

A Sampling Error is the difference between the value of a statistic obtained from an observed random sample and the value of the corresponding population parameter being estimated. Let $T$ be the sample statistic used to estimate the population parameter; the sampling error is denoted by $E$  is  $E = T −\theta$. The value of Sampling Errors reveals the precision of the estimate. The smaller the sampling error, the greater the precision of the estimate. The sampling error can be reduced:

i)   By increasing the sample size
ii)  By improving the sampling design
iii) By using the supplementary information

Non Sampling Error

The errors that are caused by sampling the wrong population of interest and by response bias, as well as those made by an investigator in collecting, analyzing, and reporting the data, are all classified as non-sampling errors or non-random errors. These errors are present in a complete census as well as in the sampling survey.

One-by-One Comparison between Sampling and Non Sampling Errors

Feature	Sampling Error	Non-Sampling Error
Definition	Occurs due to differences between the sample and the population because only a subset is observed.	Occurs due to errors in data collection, processing, or analysis, regardless of sampling.
Cause	It can occur in both sample surveys and censuses.	Mistakes in survey design, measurement errors, respondent biases, data entry errors, etc.
Occurrence	Only in sample surveys (not in censuses).	It can be reduced by increasing the sample size or using better sampling techniques (e.g., stratified sampling).
Reduction	Can be reduced by increasing the sample size or using better sampling techniques (e.g., stratified sampling).	Reduced by improving survey methods, training enumerators, better questionnaire design, and data validation.
Examples	A sample mean differs from the population mean due to random selection.	– Interviewer bias – Misrecorded responses – Non-response errors – Processing mistakes
Type of Error	Random in nature (unavoidable but measurable).	Natural variation occurs because the sample is not a perfect representation of the population.

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