Data biases refer to systematic errors or prejudices present in a dataset that can lead to inaccurate or unfair conclusions when analyzing the data.
Sampling Bias: Sampling bias occurs when certain groups within a population are more likely to be included in a sample than others, leading to skewed results.
Confirmation Bias: Confirmation bias refers to the tendency of individuals to interpret information in a way that confirms their preexisting beliefs or hypotheses.
Algorithmic Bias: Algorithmic bias occurs when machine learning algorithms produce discriminatory outcomes due to biased training datasets or flawed programming.
AP Computer Science Principles - 2.3 Extracting Information from Data
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