Description: Quantitative bias refers to a type of bias that occurs when numerical data is used in a way that distorts the true nature of the data. This phenomenon can arise at various stages of data analysis, from collection to interpretation of results. It often manifests when inappropriate metrics are selected, relevant variables are ignored, or data is presented in a way that favors a specific conclusion. Quantitative bias can have significant consequences, especially in the field of artificial intelligence (AI) and machine learning, where algorithms rely on data to learn and make decisions. If the data is biased, the resulting models can perpetuate or even amplify those distortions, leading to unfair or inaccurate decisions. This type of bias is particularly concerning in applications that affect people’s lives, such as hiring, criminal justice, and healthcare. Therefore, it is crucial to address quantitative bias through ethical practices in data collection and analysis, ensuring that appropriate statistical methods are used and that critical reviews of results are conducted to mitigate its impact.
