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This article discusses the analytical technique known as sampling and provides a brief explanation of two types of sampling analysis and how each of these methods is applied.

**What
Is Sampling Analysis?**

Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: simple random sampling and stratified random sampling.

Let’s look at both techniques in a bit more detail.

**Simple
Random Sampling**

With this method of sampling, the selection is based on chance, and every item has an equal chance of selection. An example of simple random sampling would be a lottery system.

**Example:** If we want to come up with
the average value of all cars in the United States, it would be impractical to
find every car, assign a value, and then develop an average. Instead, we might
randomly select 200 cars, get a value for those cars, and then find an average.
The random selection of those 200 cars would be the “sample data of the entire
United States” cars’ values (population data).

**Pros
and Cons of Simple Random Sampling**

**Pros:** Economical in nature, less
time consuming

**Cons:** Chance of bias, the difficulty
of getting a representative sample

**Stratified
Random Sampling**

Here, the population data is divided into subgroups known as strata. The members in each of the subgroups have similar attributes and characteristics in terms of demographics, income, location, etc. A random sample from each of these subgroups is taken in proportion to the subgroup size relative to the population size. These subsets of subgroups are then added to a final stratified random sample. Improved statistical precision is achieved through this method due to the low variability within each subgroup and the fact that a smaller sample size is required for this method as compared to simple random sampling. This method is used when the researcher wants to examine subgroups within a population.

**Example:** One might divide a sample of
adults into subgroups by age groups, like 18-29, 30-39, 40-49, 50-59, and 60
and above. To stratify this sample, the researcher would then randomly select
proportional amounts of people from each age group. This is an effective
sampling technique for studying how a trend or issue might differ across
subgroups. Some of the most common strata used in stratified random sampling
include age, gender, religion, race, educational attainment, socioeconomic
status, and nationality. With stratified sampling, the researcher is guaranteed
that the subjects from each subgroup are included in the final sample, whereas
simple random sampling does not ensure that subgroups are represented equally
or proportionately within the sample.

**Pros
and Cons of Stratified Random Sampling**

**Pros:** Economical in nature, less
time consuming, less chance of bias as compared to simple random sampling, and higher
accuracy than simple random sampling

**Cons:** Need to define the
categorical variable by which subgroups should be created — for instance, age
group, gender, occupation, income, education, religion, region, etc.

Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. Sampling is useful in assigning values and predicting outcomes for an entire population based on a smaller subset or sample of the population. The organization will choose either the simple random sampling or the stratified random sampling method, based on the type of data, the need for accuracy and representation of certain subsets and groups, and other analytical requirements of the organization.