Biases
In data science, biases refer to the systematic errors that can occur during data collection, analysis, and interpretation, leading to inaccurate or unfair results. Biases can be introduced by various factors, such as the selection of the sample, the measurement instruments used, the data preprocessing techniques applied, or the algorithms and models employed. Biases can be classified into different types, such as sampling bias, measurement bias, confirmation bias, or algorithmic bias, depending on their origin and impact. Biases can have serious consequences, especially in sensitive domains, such as healthcare, finance, or justice, where they can perpetuate discrimination, inequality, or injustice. Therefore, detecting, mitigating, and preventing biases is a crucial task in data science, requiring a combination of technical, ethical, and social skills and knowledge.
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