Stratified random sampling is a method of selecting a sample in which the sample is first divided into strata various strata are selected from the sample The population is first divided into strata, and then random samples are drawn from each stratum None of the above

Stratified random sampling is a method of selecting a sample in which the population is first divided into strata, and then random samples are drawn from each stratum. This method is used when the population is not homogeneous or evenly distributed. It ensures that each subgroup within the population receives proper representation within the sample.

Here are the steps involved in stratified random sampling:

1. Identify the Population: The first step is to identify the set of people or objects that you want to study. This is your population.

2. Classify the Population into Strata: The next step is to divide your population into strata or groups. Each stratum should be mutually exclusive, meaning that every element in the population should be included in one and only one stratum. The strata are often formed based on some characteristics of the population elements.

3. Determine the Sample Size: Decide on the total number of population elements that you want to include in your sample. This is your sample size.

4. Select Random Samples from Each Stratum: For each stratum, you randomly select elements to include in your sample. The number of elements that you select from each stratum can be proportional to the size of the stratum.

5. Collect Data: Once you have selected your sample, you can then collect data from these elements.

The option that best describes stratified random sampling is: "The population is first divided into strata, and then random samples are drawn from each stratum."