# For stratified random sampling:

If you are using stratified sampling you may need to adjust your strata and collapse into smaller strata if you find that some of your sample sizes are too small.

## Describes simple random sampling.

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In stratified sampling, the population is partitioned into non-overlapping groups, called strata and a sample is selected by some design within each stratum.

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For example, geographical regions can be stratified into similar regions by means of some known variable such as habitat type, elevation or soil type. Another example might be to determine the proportions of defective products being assembled in a factory. In this case sampling may be stratified by production lines, factory, etc.

## Limitations of Stratified Random Sampling

The preceding section has covered the most common problems associated with statistical studies. The desirability of a sampling procedure depends on both its vulnerability to error and its cost. However, economy and reliability are competing ends, because, to reduce error often requires an increased expenditure of resources. Of the two types of statistical errors, only sampling error can be controlled by exercising care in determining the method for choosing the sample. The previous section has shown that sampling error may be due to either bias or chance. The chance component (sometimes called random error) exists no matter how carefully the selection procedures are implemented, and the only way to minimize chance sampling errors is to select a sufficiently large sample (sample size is discussed towards the end of this tutorial). Sampling bias on the other hand may be minimized by the wise choice of a sampling procedure.

## With stratified random sampling the population ..

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