Simple random sampling

Revision as of 15:47, 3 December 2010

General observations

Simple random sampling (SRS) is the basic theoretical sampling technique. The sampling elements are selected as an independent random sample from the population. Each element of the population has the same probability of being selected. And, likewise, each combination of n sampling elements has the same probability of being eventually selected.

Every possible combination of sampling units from the population has an equal and independent chance of being in the sample.

Simple random sampling is introduced and dealt with here and in sampling textbooks mainly because it is a very instructive way to learn about sampling; many of the underlying concepts can excellently be explained with simple random sampling. However, it is hardly applied in forest inventories because there are various other sampling techniques which are more efficient, given the same sampling effort.

For information about how exactly sampling units are choosen see Random selection.

Notations used

Estimators	Parametric	Sample
Mean	\(\mu = \frac{\sum_{i=1}^N y_i}{N}\)	<mat>\bar {y} = \frac{sum_{i=1}^n y_i}{n}</math>
Variance	\(\sigma^2 = \frac{\sum_{i=1}^N (y_i - \mu)^2}{N}\)	\(s^2 = \frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}\)
Standard deviation	\(\sigma = \sqrt{\frac{\sum_{i=1}^N (y_i - \mu)^2}{N}}\)	\(s = \sqrt{\frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}}\)
Standard error (without replacement or from a finite population)	\(\sigma_{\bar {y}} = \sqrt{\frac{N-n}{N-1}}*\frac {\sigma}{\sqrt{n}}\)	\(S_{\bar {y}} = \sqrt{\frac{N-n}{N}}*\frac{S_y}{\sqrt{n}}\)
Standard error (with replacement or from an infinite population)	\(\sigma_{\bar {y}} = \frac{\sigma}{\sqrt{n}}\)	\(S_{\bar {y}} = \frac{S_y}{\sqrt{n}}\)

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