What is Selection Bias and what are the various types?

Data Science, AI and ML
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Selection bias is typically associated with research that doesn’t have a random selection of participants. It is a type of error that occurs when a researcher decides who is going to be studied. On some occasions, selection bias is also referred to as the selection effect.

In other words, selection bias is a distortion of statistical analysis that results from the sample collecting method. When selection bias is not taken into account, some conclusions made by a research study might not be accurate.

The following are the various types of selection bias:

  • Sampling Bias: A systematic error resulting due to a non-random sample of a populace causing certain members of the same to be less likely to be included than others that results in a biased sample
  • Time Interval: A trial might end at an extreme value, usually due to ethical reasons, but the extreme value is most likely to be reached by the variable with the most variance, even though all variables have a similar mean
  • Data: Results when specific data subsets are selected for supporting a conclusion or rejection of bad data arbitrarily