Disadvantages and Advantages of Sampling and Random Sampling
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Disadvantages and Advantages of Sampling and Random Sampling
The data sampling technique is a statistical analysis method utilized in the selection, manipulation, and analysis of a representative data subset points for the identification of trends and patterns in large data sets (Aounallah et al., 2004). Data processing utilizes data sampling since processing an entire data set in time-consuming and expensive. The sampling types used in the reduction of data include sampling with and without replacement as well as simple random sampling. In simple random sampling, each object has an equal probability of being selected. However, in sampling without replacement, data objects chosen are taken from the data population. In sampling with replacement, researchers chose data objects more than once from the original data set population.
The advantages of sampling methods include saving time by reducing data volume and avoiding redundancy in querying individual data objects (Aounallah et al., 2004). Also, data sampling allows the achievement of accurate results in a limited amount of time while obtaining detailed data through the use of a few resources. The disadvantages of sampling techniques include the likelihood of bias since it is dependent on a person’s judgment and mindset. Another disadvantage of sampling is that selecting a sample may become dysfunctional due to the inappropriate selection of the sampling method. The other disadvantage of sampling is the high probability of excluding heterogeneous data, which may affect the accuracy of the results.
In sample random sampling without replacement, data values have an equal probability of being chosen (Frerichs, 2008). Also, in the random selection of data values without replacement, the randomly picked data value is not taken back to the original dataset after being chosen to avoid drawing the same data value. Selecting samples randomly without replacement is a good approach in sampling since the sampling process avoids duplication and the multiple tallying of data sets objects. Also, random sampling without replacement reduces the variability data set value means, which increases the efficiency of the sampling process during sampling of large data sets.
References
Aounallah, M., Quirion, S., & Mineau, G. W. (2004, May). Distributed data mining vs. sampling techniques: A comparison. In Conference of the Canadian Society for Computational Studies of Intelligence (pp. 454-460). Springer, Berlin, Heidelberg.
Frerichs, R. R. (2008). Simple random sampling. Rapid Surveys. Unpublished.