Systematic Error
In Data Science, Systematic Error refers to a type of error that occurs consistently in the same direction and magnitude in a dataset. It is caused by flaws in the data collection process or measurement tools, and can lead to biased results and inaccurate conclusions. Systematic errors can be identified and corrected by adjusting the measurement tools or data collection methods, or by using statistical techniques such as calibration or regression analysis. Examples of systematic errors in data science include measurement bias, selection bias, and confounding variables.
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