# Simple Random Sample

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## Definition of 'Simple Random Sample'

A simple random sample is a subset of a population in which each member of the population has an equal chance of being included. This is in contrast to a stratified random sample, in which the population is divided into strata and a random sample is taken from each stratum.

Simple random sampling is the most basic and unbiased type of sampling. It is often used when the population is large and it is not possible to take a census of the entire population.

To take a simple random sample, you can use a random number generator to select the members of the sample. Alternatively, you can use a table of random numbers or a lottery system.

Once you have selected the members of the sample, you can collect data from them. The data from the sample can then be used to make inferences about the population as a whole.

Simple random sampling is a very effective way to collect data from a population. However, it is important to note that it is not always possible to take a simple random sample. For example, if the population is not well-defined or if the members of the population are not accessible, it may be impossible to take a simple random sample.

In such cases, it may be necessary to use a different type of sampling, such as a stratified random sample or a cluster sample.

Here are some of the advantages of using simple random sampling:

* It is the most unbiased type of sampling.

* It is relatively easy to implement.

* It is relatively inexpensive.

Here are some of the disadvantages of using simple random sampling:

* It is not always possible to take a simple random sample.

* It may not be possible to collect data from all members of the sample.

* The results of a simple random sample may not be representative of the population as a whole.

Simple random sampling is the most basic and unbiased type of sampling. It is often used when the population is large and it is not possible to take a census of the entire population.

To take a simple random sample, you can use a random number generator to select the members of the sample. Alternatively, you can use a table of random numbers or a lottery system.

Once you have selected the members of the sample, you can collect data from them. The data from the sample can then be used to make inferences about the population as a whole.

Simple random sampling is a very effective way to collect data from a population. However, it is important to note that it is not always possible to take a simple random sample. For example, if the population is not well-defined or if the members of the population are not accessible, it may be impossible to take a simple random sample.

In such cases, it may be necessary to use a different type of sampling, such as a stratified random sample or a cluster sample.

Here are some of the advantages of using simple random sampling:

* It is the most unbiased type of sampling.

* It is relatively easy to implement.

* It is relatively inexpensive.

Here are some of the disadvantages of using simple random sampling:

* It is not always possible to take a simple random sample.

* It may not be possible to collect data from all members of the sample.

* The results of a simple random sample may not be representative of the population as a whole.

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