Need for sampling

The economic advantages of taking a sample rather than a census are massive. Consider the cost of taking a census.

1. Why should we spend thousands of shillings interviewing all 4,000 employees in our company if we can find out what we need to know by asking only a few hundred?
2. Deming argues that the quality of a study is often better with sampling than with a census. He suggests, ‗Sampling possesses the possibility of better interviewing (testing), more thorough investigation of missing, wrong, or suspicious information. Research findings substantiate this opinion.
3. Sampling also provides much quicker results than does a census. The speed of execution reduces the time between the recognition of a need for information and the availability of that information.
4. Some situations require sampling. When we test the breaking strength of materials, we must destroy them; a census would mean complete destruction of all materials. Sampling is also the only process possible if the population is infinite.
5. In few cases, it would be impossible or dangerous to use whole population, i.e., testing of vaccine for AIDs – could result in death.

The advantages of sampling over census studies are less compelling when the population is small and the variability is high. Two conditions are appropriate for a census study: A census is
1. Feasible when the population is small and
2. Necessary when the elements are quite different from each other.

When the population is small and variable, any sample we draw may not be representative of the population from which it is drawn. The resulting values we calculate from the sample are Incorrect as estimates of the population values. When the sample is drawn properly, however, some sample elements underestimate the parameters and others overestimate them. Variations in these values counteract each other, this counteraction results in a sample value that is generally close to the population value. For these offsetting effects to occur, however, there must be enough members in the sample, and they must be drawn in a way to favour neither overestimation nor underestimation.

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