Sampling Bias
Sampling bias is a type of bias that occurs when a sample is collected in such a way that some members of the intended population are more likely to be included than others. This can lead to inaccurate or misleading conclusions about the population. In data science, sampling bias can occur when the data used to train a model is not representative of the population it is intended to predict. This can result in a model that performs poorly on certain segments of the population, leading to unfair or biased outcomes. Sampling bias can be mitigated by using random sampling techniques and ensuring that the sample is representative of the population.
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