What is random sampling? How to implement it in the research?

Random sampling is a probability sampling method where every member of the population possesses an equal and known chance of being selected for inclusion in the study sample. Its implementation in research ensures unbiased selection and supports statistical inference. Effective random sampling hinges on several key principles. A comprehensive and accurate sampling frame enumerating all population elements is essential. Genuine randomness, typically achieved using random number generators or lottery methods, must govern the selection process. The sample size must be sufficient to achieve the desired statistical power. Representativeness, enabling generalizations from the sample back to the entire population, is the core objective, contingent upon unbiased application of the random selection procedure. Violating randomness introduces selection bias, compromising validity. To implement random sampling, first clearly define the target population and establish a complete sampling frame. Next, determine the required sample size based on statistical considerations. Then, apply a recognized random selection method (e.g., simple random sampling using random numbers, systematic sampling with a random start) to choose participants from the frame. Finally, collect data from the selected individuals. This method is fundamental in surveys, experiments, and quantitative studies aiming for unbiased population estimates.