Why Random Sampling Does Not Guarantee Representativeness
Random sampling is a method for obtaining a representative subsample from a large dataset. In this process, each sample element has a uniform probability distribution. The sample must be large enough to estimate the parameters of the population under study (Witte and Witte). The sample can be random in variable values, content, or time. Each element of the selected subsample should be individual and independent of other elements.
The sample should be evenly distributed across all sections of the population. This ensures that the data are representative and can be used to study the population as a whole. Thus, random sampling helps generate representative data for further study. In addition, the random sampling process ensures that the selected sample is representative of the entire population.
However, this statement is not valid. Even when random selection is conducted correctly, errors can still occur because the selected sample may not be large enough or may not accurately represent all available options. Additionally, the impact of this error may persist and continue to affect the study results.
Factors and Variables Influencing the Outcomes of Studies
Study Design
Other factors and variables may also influence the research results, in addition to random sampling. For example, the type of research—survey, experiment, or monitoring—can affect the study results. An essential factor is the study’s design, namely the variables, their ranges, and the relationships between variables (Witte and Witte). This can influence the obtaining of reliable data and maximize the accuracy of the research results.
Data Collection
Another factor variable is the method of data collection. For example, using the internet to collect data may result in results that are not entirely reliable and biased. Thus, the random sampling process is not the only factor guaranteeing representative data for the study population. To achieve the most accurate research results possible, it is necessary to consider a wide range of variables that may influence the results.
Work Cited
Witte, Robert, and John Witte. Statistics. 11th ed. John Wiley & Sons, 2017.