Random Sampling
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Random Sampling
Simple random sampling can simply be defined as a technique used in sampling where every item in the entire population has an equal chance and probability of being selected in the sample. This method only works with a small random portion of the entire population. Apparently, Simple random sampling is the best method of data collection where the population is large. For example, in a case where the entire population is 250 people, every person has an equal chance and likelihood of being selected. Due to this, the sampling method is also referred to as the method of chances (Lone & Tailor, 2017). Numerous benefits are associated with the use of simple random sampling. The first one is that it is not expensive as compared to other methods of data collection, the second one is that it represents the entire population and it is also accurate and time friendly.
It is a matter of the fact that it is possible for one to sample data instances through the use of a distribution that is different from the uniform distribution. A uniform distribution is where all the results are correspondingly likely. This means that the probability of getting either side or result is the same. For instance, when rolling a coin or a die, the probability of getting either side of the coin or die is the same (West, 2016). This means that the likelihood of getting any of the sides is pre-determined. To this extent, therefore, we can simply conclude that when using a uniform distribution, any outcome is possible since chances are equal. The probability of getting the head is equal to that of getting the tail.
References
Lone, H. A., & Tailor, R. (2017). Estimation of population variance in simple random sampling. Journal of Statistics and Management Systems, 20(1), 17-38.
West, P. W. (2016). A simple random sampling of individual items in the absence of a sampling frame that lists the individuals. New Zealand Journal of Forestry Science, 46(1), 1-7.