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## Stratified sampling

When we select a limited number of elements from large group of elements population for sampling, we want to make sure that the samples taken correctly represent the population. How much our analysis of the limited dataset agrees with the characteristics of the population depends largely on the method of sampling used. One way of selecting samples from the population is by dividing the whole population into small strata consisting up of elements with some similar characteristics and then choosing such number of samples from each of them so as to proportional to the size of the stratum. This method of sampling is called Stratified Random Sampling and it is a kind of Probability Sampling. The above figure shows how different types of items are distributed in a random population. We need to stratify the population.

When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study. Ensures a high degree of representativeness of all the strata or layers in the population. Possibly, members of units are different from one another, decreasing the techniques effectiveness. Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups strata according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata. Accordingly, application of stratified sampling method involves dividing population into different subgroups strata and selecting subjects from each strata in a proportionate manner. The table below illustrates simplistic example where sample group of 10 respondents are selected by dividing population into male and female strata in order to achieve equal representation of both genders in the sample group. Stratified sampling can be divided into the following two groups: proportionate and disproportionate.

When experimenters or researchers are looking for data, it is often impossible to measure every individual data point in a population. However, statistical methods allow for inferences about a population by analyzing the results of a smaller sample extracted from that population. There are several methods of sampling. Stratified random sampling is one common method that is used by researchers because it enables them to obtain a sample population that best represents the entire population being studied, making sure that each subgroup of interest is represented. All the same, this method of research is not without its disadvantages. Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. A disadvantage is when researchers can't classify every member of the population into a subgroup. Explicit stratified sampling, on the other hand, might involve sorting people into a number of age groups and then randomly sampling 1 in people from each.

## Stratified Random Sampling: Definition, Method and Examples

In statistics , stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys , when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation stratum independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling.

Simple random sampling means that every member of the sample is selected from the group of population in such a manner that the probability of being selected for all members in the study group of population is the same. Image: Simple random sampling. In other words, sampling units are selected at random so that the opportunity of every sampling unit being included in the sample is the same. This is the basic method of sampling. Among its disadvantages are the following: It is not as random as other methods.

И одновременно пустит АНБ ко дну. Сьюзан внезапно подумала, что Хейл, возможно, говорит правду, но потом прогнала эту мысль. Нет, решила .

Беккер грохнулся на пол возле двери. Мостовая стремительно убегала назад в нескольких дюймах внизу. Он окончательно протрезвел.

Далее в заметке сообщалось, что, хотя алгоритм вызвал громадный интерес в Японии, несколько американских производителей программного обеспечения, прослышавших о Цифровой крепости, считают эту информацию нелепой - чем-то вроде обещания превратить свинец в золото. Формула, утверждают они, - это мистификация, к которой не следует относиться серьезно. - Аукцион? - Сьюзан подняла .

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1. Egle B.

Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. A disadvantage is when researchers can't classify every member of the population into a subgroup.