There are several advantages of sampling over census (i.e. selection of whole
population for analysis).
Firstly, the costs on sampling should be much lower than that on census. For example,
for the government by-census (note: population census is usually conducted once
every ten years and a by-census is conducted in the middle of the intercensal period),
one fifth of the population is large enough to declare what the government wants to
know. There is no need to spend several times of dollars to interview the entire
population in the society.
Secondly, a quality guru (Deming, 1960) argued that the quality of a study was often
better with sampling than with a census. He suggested that, "Sampling possesses the
possibility of better interviewing(testing), more thorough investigation of missing,
wrong , or suspicious information, better supervision, and better processing than is
possible with complete coverage". Research findings substantiate this opinion. More
than 90% of survey error in one study was from non-sampling error1, and 10% or less
was from sampling error2. (Donald et al., 1995)
Thirdly, sampling can save the time. The speed of execution reduces the time between
the recognition of a need for information and the availability of that information.
1 Non-sampling error is the error of research due to factors other than the sample size and sampling
method, including non-response, bad communication with interviewees, measurement error, etc.
2 Sampling error is the error during research due to the sample size and sampling method.
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It is more accurate, unbiased and includes every item in the population, whereas sampling may be biased, and sampling is not totally representative.
Less time and less cost for a sample
The limitation of any statistical application is the fact that it always depends on some sampling. If this sampling is incomplete, or not representative of your actual base (say customers), your data may not yield accurate conclusions. Additionally, since it depends on sampling, your measurement is only applicable to a base that fits the profile of your sampling. For instance, if you took your sampling from Caucasian males over age 35, and it turns out your customer base has a large number of African, American females under 23, your conclusions based on the statistics could be imperfect.
Sampling error occurs when the sampling protocol does not produce a representative sample. It may be that the sampling technique over represented a certain portion of the population, causing sample bias in the final study population.