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Random sampling is a statistical technique used to obtain a representative sample from a larger population. It involves randomly selecting individuals or units from the population, meaning each member has an equal chance of being chosen (Elfil & Negida, 2017). This method is particularly useful when the population under study is large and diverse, and a complete enumeration is not feasible or practical. Random sampling helps to ensure that the sample is unbiased and that the results obtained from it can be generalized to the larger population with a high degree of accuracy (Noor et al., 2022). This technique is widely used in research studies, surveys, and opinion polls to draw valid conclusions about the population being studied.

In order to ensure that patient satisfaction surveys provide an accurate representation of the population, it is important to use a random sampling technique. One effective way to achieve this is by selecting participants based on their unique medical record Number (MRN). By doing so, the survey results will incorporate a diverse range of individuals without any bias towards factors such as age, sex, race, or medical condition. This approach enhances the validity and reliability of the survey data, which can ultimately inform improvements in healthcare services and patient care.

The random sampling method is one of the most commonly used techniques in patient satisfaction surveys (Suresh et al., 2011). However, one of its weaknesses is that it may not be able to provide an accurate representation of the population if the patients are not selected properly. This happens when the sample size is too small, the patients are unable to participate in the survey, or the sampling technique used is not appropriate. When this occurs, the results of the survey may not be reliable, and the conclusions drawn from them may not be valid.