Selection Bias
Selection bias is a type of bias that occurs when the sample data used in a study is not representative of the population being studied. This can happen when the selection process for the sample is flawed or when certain groups are overrepresented or underrepresented in the sample. Selection bias can lead to inaccurate or misleading conclusions and can affect the generalizability of the study's findings. In data science and artificial intelligence, selection bias can occur when the data used to train a model is not representative of the population it will be applied to. This can result in a model that performs poorly on new data or that produces biased predictions. To mitigate selection bias, it is important to carefully consider the sampling process and to use techniques such as stratified sampling or random sampling to ensure that the sample is representative of the population.
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