![]() For the purposes of this blog we will be focusing on random sampling methods. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. Random sampling examples include: simple, systematic, stratified, and cluster sampling. We could choose a sampling method based on whether we want to account for sampling bias a random sampling method is often preferred over a non-random method for this reason. What makes a good sample?Ī good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study. In other words, it is a list from which we can extract a sample. ![]() What is a sampling frame?Ī sampling frame is a record of the target population containing all participants of interest. However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding. It is important to understand why we sample the population for example, studies are built to investigate the relationships between risk factors and disease. This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.
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