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WELCOME TO MY NEW TOPIC 👇

'SAMPLING ERROR'

learning objectives

 To understand about samping and different types of sampling errors

samping error

 Sampling Error

Sampling is the process of selecting observation (a sample) to provide an 

advocate description of the population and inference of the population.

Sampling error refers to the difference between the sample and population that 

exist only because of the observations that happened to be selected for the 

sample. The sample size will reduce this type of error.

What Is a Sampling Error?

A sampling error is a statistical error that occurs when an analyst does not 

select a sample that represents the entire population of data. As a result, the 

results found in the sample do not represent the results that would be obtained 

from the entire population.

Sampling is an analysis performed by selecting several observations from a 

larger population. The method of selection can produce both sampling errors 

and non-sampling errors.

Understanding Sampling Errors

A sampling error is a deviation in the sampled value versus the true population 

value. Sampling errors occur because the sample is not the representative of the 

population or is biased in some way. Even randomized samples will have some 

degree of sampling error because a sample is only an approximation of the 

population from which it is drawn.

Calculating Sampling Error

The sampling error formula is used to calculate the overall sampling error in 

statistical analysis. The sampling error is calculated by dividing the standard 

deviation of the population by the square root of the size of the sample, and 

then multiplying the resultant with the Z-score value, which is based on the 

confidence interval.

Sampling Error= Z × n​σ where: Z is 

score value based on the confidence interval (approx.=1.96) 

σ=Population, standard deviation, n =Size of the sample​

Types of Sampling Errors

There are different categories of sampling errors.

Population-Specific Error

A population-specific error occurs when a researcher doesn't understand who to 

survey.

Selection Error

Selection error occurs when the survey is self-selected, or when only those 

participants who are interested in the survey respond to the questions. 

Researchers can attempt to overcome selection error by finding ways to 

encourage participation.

Sample Frame Error

A sample frame error occurs when a sample is selected from the 

wrong population data.

Non-response Error

A non-response error occurs when a useful response is not obtained from the 

surveys because researchers were unable to contact potential respondents (or 

potential respondents refused to respond).

Eliminating Sampling Errors

The prevalence of sampling errors can be reduced by increasing

 The sample size. As the sample size increases, the sample gets closer to the 

actual population, which decreases the potential for deviations from the actual 

population. Consider that the average of a sample of 10 varies more than the 

average of a sample of 100. Steps can also be taken to ensure that the sample 

adequately represents the entire population.

Researchers might attempt to reduce sampling errors by replicating their study. 

This could be accomplished by taking the same measurements repeatedly, 

using more than one subject or multiple groups, or by undertaking multiple 

studies.

Random sampling is an additional way to minimize the occurrence of sampling 

errors. Random sampling establishes a systematic approach to selecting a 

sample. For example, rather than choosing participants to be interviewed 

haphazardly, a researcher might choose those whose names appear first, 10th, 

20th, 30th, 40th, and so on, on the list.

Key points

• A sampling error occurs when the sample used in the study is not

 

representative of the whole population

 

• Sampling is an analysis performed by selecting several 

 

observations from a larger population.

 

• Even randomized samples will have some degree of sampling 

 

error because a sample is only an approximation of the population 

from which it is drawn.

 

• The prevalence of sampling errors can be reduced by increasing 

 

the sample size.

 

• In general, sampling errors can be placed into four categories

 

population-specific error, selection error, sample frame error, or 

 

non-response error.

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