Selection bias is the distortion that arises when the individuals, groups, or data chosen for analysis are not representative of the larger population due to non-random selection. This can lead to inaccurate conclusions in statistical studies because the sample may not accurately reflect the characteristics of the population intended to be analyzed.
For instance, if a medical study only includes participants from a particular hospital that specializes in severe cases, the findings might not be applicable to the general population, which includes individuals with varying degrees of severity.
To mitigate selection bias, researchers should ensure that samples are drawn randomly and are representative of the population. Additionally, techniques such as stratification or using weights to adjust for unequal probabilities of selection can also help.