Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. It is the distortion of statistical analysis accuracy resulting from the non-randomized method of collecting samples.
Types of selection bias include:
- Sampling bias, which is an error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
- Time interval bias, which means a trial may be terminated early at an extreme value, but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
- Data bias, which occurs when specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds.
- Attrition bias is caused by a loss of participants discounting trial subjects/tests that did not run to completion. It is closely related to survivor ship bias, where the only subjects that survive a process are included in the analysis.