It is a statistical error that causes a bias in the sampling portion of an experiment.
The error causes one sampling group to be selected more often than other groups included in the experiment.
Selection bias may produce an inaccurate conclusion if the selection bias is not identified.

Selection bias occurs when the data used to train a machine learning model is not representative of the population it is supposed to represent. This can happen if the sampling method used to collect the data systematically favors certain outcomes or characteristics over others, leading to a skewed or incomplete view of the underlying population. As a result, the model may learn patterns or relationships that do not generalize well to new, unseen data, leading to inaccurate predictions or biased conclusions. Selection bias can undermine the validity and reliability of a machine learning model, making it essential to carefully consider the representativeness of the training data and to employ appropriate techniques to mitigate bias whenever possible.