Simple random samples of these plots were selected within each region (Fig.

Survey data were analyzed by region as simple random samples, except British Columbia, where the sample design was stratified with optimum allocation of effort and could not be treated as a simple random sample.

Thus, these data represent complex samples rather than simple random samples. Researchers, therefore, cannot conduct their data analyses as they would when using a simple random sample.

The design effects (i.e., root design effect [DEFT] and design effect [DEFF]) measure the impact of the departure of the study's complex sample design from a simple random sample design.

Because each item in a population has an equal probability of selection on a given draw, simple random samples are considered to be highly representative.

In a simple random sample, every item in a population has the same probability of selection.

All groups are simple random samples from their respective populations.

Each group is a simple random sample from its population.

Let [X.sub.i] and [Y.sub.i] be

simple random samples (i=1,..., n) from a population of size N with mean [[micro].sub.x] and [[micro].sub.y], and X and Y be two sample means.

With a sampling distribution, we can consider how such statistics as the mean or the variance may vary from sample to sample if we draw repeated simple random samples of the same size from the same population.

Thus, cluster sampling is usually more efficient than simple random sampling when the population to be sampled is scattered over a wide area and considerable travel costs would be involved in drawing the simple random sample.

Let [X.sub.i] and [Y.sub.i] be

simple random samples (i=1, ..., n) from a population of size N with mean [[mu].sub.x] and [[mu].sub.y], and [bar]X and [bar]Y be two sample means.