Page 163 - Data Science class 11
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Random sampling ensures that results obtained from your sample should approximate what would have been
obtained if the entire population had been measured (Shadish et al., 2002). The simplest random sample allows all
the units in the population to have an equal chance of being selected.
Simple Random Sampling method is one of the best probability sampling techniques that helps in saving time and
resources. It is a reliable method of obtaining information.
Steps to Create a Simple Random Sample
To create a simple random sample, there are six steps:
1. defining the population
2. choosing size for your sample
3. listing the population
4. assigning numbers to the units
5. finding random numbers
6. selecting sample
Example
An example of a simple random sample could be the names of 30 students being chosen from a classroom strength
of 300. In this case, the population is 300 students and the sample is random because each student has an equal
chance of being chosen.
Advantages
Following are the advantages of simple random sampling:
• Unbiased: Since individuals are chosen at random from the larger group set, each individual in the large population
has the same probability of being selected. In most cases, this creates a balanced subset that carries a huge capacity
to represent the larger group as a whole.
• Simple to apply: This method involves dividing larger groups into smaller subgroups based on any attributes they
share. The individuals are randomly selected and this doesn’t require any special skills. Thereafter, there are no
additional steps.
• No prerequisite knowledge needed: Researchers don't need to have any prior information or knowledge about the
larger population.
Disadvantages
Following are the disadvantages of simple random sampling:
• Time consuming: Sample random sampling is time-consuming especially when a full list of a large population is
not accessible or available. In that case, researchers might have to look for other sources of information. A list of
population subset can be used to recreate the entire population list, but this might take time.
• Costly: The third-party data providers often charge for providing database or lists associated with population or its
subsets.
Stratified Sampling
Stratified random sampling differs from simple random sampling, which involves the random selection of data from
an entire population, so each possible sample is equally likely to occur.
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 by dividing population into subgroups called strata. This method of statistical sampling,
however, cannot be used in every study design or with every data set.
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