005. The Importance of Sampling

Samples are necessary because populations are often too big to study in their entirety. It is too time-consuming and costly to examine the entire population, that a selection of a sample from the population, calculate the sample statistic, and use it to estimate the corresponding population parameter often is very helpful.

There’re Two branches of statistical analysis:

1. Descriptive statistics is the process of collecting, organizing, and presenting data in some manner that quickly and easily describes these data.

2. Inferential statistics involves the use of a sample to draw some inference, or conclusion, about the population from which that sample was taken.

However, all too often, the sample proves not to be very representative of the population, and Sampling error will result. Because due to the luck of the draw in selecting the sample elements, it’s possible to unknowingly choose atypical elements that misrepresent the population. So, Sampling error is the difference between the unknown population parameter and the sample statistic used to estimate the parameter.

Sampling bias is the tendency to favor the selection of certain elements over others. It’s a more serious form of sampling error.

If the sampling procedure is incorrectly designed and tends to promote the selection of too many units with a particular characteristic at the expense of units without that characteristic, the sample is said to be biased. For Example, the sampling process may inherently favor the selection of males to the exclusion of females, or married persons to the exclusion of singles.

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