# Stratified Random Sampling: How It Works and Examples.

. How Stratified Random Sampling Works:

Stratified random sampling is a method of selecting a sample from a population in which the population is divided into strata, and a random sample is taken from each stratum. This type of sampling is often used when the population is heterogeneous, and it is desired to have a representative sample.

#### What is the main objective of using stratified random sampling Brainly?

The main objective of using stratified random sampling is to ensure that each subgroup within the population is represented proportionately in the sample. This technique is often used when there is a large population with many different subgroups, and it is important to make sure that each subgroup is accurately represented in the sample.

#### What is sampling in analytical chemistry?

In analytical chemistry, sampling is the process of taking a small representative portion of a material for analysis. This is usually done in order to avoid having to analyze the entire batch of material, which can be time-consuming and expensive. Sampling is an important part of many analytical procedures, and careful consideration must be given to the best way to obtain a representative sample.

##### How do you use stratified random sampling in Excel?

There are a few different ways to do stratified random sampling in Excel. One way is to use the RANDBETWEEN function. This function will return a random integer between two numbers that you specify. For example, if you want to generate a random number between 1 and 10, you would use the following formula: =RANDBETWEEN(1,10).

To use the RANDBETWEEN function for stratified random sampling, you would first need to create a list of the different strata that you want to sample from. For example, let's say you want to sample from a population of people that is divided into three strata: men, women, and children. You would then need to create a list of all the people in each stratum. So, your list might look something like this:

Men:

John

Mike

Tom

women:

Mary

Susan

Lisa

children:

Billy

Sarah

Once you have your list, you can use the RANDBETWEEN function to generate a random number for each stratum. So, for example, if you wanted to generate a random number between 1 and 3 for the men's stratum, you would use the following formula: =RANDBETWEEN(1,3). This would give you a random number between 1 and 3 for the men's stratum. You would then use this same formula for the women's and children's strata.

Once you have a random number for each stratum, you can use this number to select a random person from each stratum. So, using our example above, if the random number generated for the men's stratum was 2, this would mean that you would select the second person on the men's list, which would be Mike. You would then do the same for the women's and children's strata What is the role of sampling in economic analysis? Sampling plays an important role in economic analysis as it allows analysts to make inferences about a population based on a smaller, more manageable sample. In many cases, it is not possible or practical to survey or study an entire population, so sampling provides a way to obtain information that can be used to draw conclusions about the population as a whole.

There are a variety of ways to select a sample, and the chosen method will depend on the type of data being collected and the goals of the analysis. For example, if the goal is to estimate the average income of a population, a simple random sample would be used. This type of sample is easy to generate and provides a good representation of the population, but it may not be the most efficient way to collect data if the population is very large.

In general, analysts want to select a sample that is as representative as possible of the population of interest. This can be challenging, as there are often many different ways to define a population. For instance, when studying the U.S. economy, one could focus on all consumers, all businesses, or all workers. Each of these groups would likely have different characteristics and behave differently in the economy, so the choice of population can have a big impact on the findings of the analysis.

Once a sample has been selected, data can be collected from the individuals or units in the sample. This data can then be used to estimate the characteristics of the population, such as the mean or median income. In some cases, the data may also be used to test hypotheses about the population, such as whether the average income is different for two different groups.

Sampling is an important tool in economic analysis, as it allows analysts to make inferences about a population based on a smaller, more manageable sample. Sampling can be used to estimate the characteristics of a population or to test hypotheses about the population. In order to select an appropriate sample, analysts must first Where is stratified random sampling used? There is no definitive answer to this question as stratified random sampling can be used in a variety of different settings and for a variety of different purposes. However, some examples of where stratified random sampling might be used include when conducting market research surveys and when designing clinical trials.