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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