Glossary term
Sampling Error: Meaning and Definition
Reviewed and approved by Dr. Anamika ShrivastavaPublished Last updated
Sampling error is the difference between the value estimated from a sample and the true value for the whole population. It arises because only part of the population is studied, and it generally shrinks as the sample gets larger. It is separate from non-sampling errors such as bias.
In more detail
Sampling error arises only because a sample is not the whole population. It is random in direction and shrinks as the sample grows, roughly in proportion to the square root of the sample size. It differs from bias, which comes from faulty design and does not shrink with a larger sample.
Example
If the average income of a population is ₹20,000 and a sample of three gives a mean of ₹24,670, the sampling error is ₹4,670.
Key points
- Error = sample estimate − population value.
- Falls as the sample size increases.
- A census has no sampling error.
Common mistake
Believing a larger sample removes all error. Bias and non-response remain.
Related terms
Where it is explained
This term is explained in Class 11 Statistics Chapter 2: Collection of Data.