Statistical Independence
In the context of Data Science, Statistical Independence is a fundamental concept that refers to the relationship between two or more random variables. When two variables are statistically independent, it means that the occurrence of one does not affect the probability of the occurrence of the other. In other words, the events represented by the variables do not influence each other. This concept is crucial in many areas of data science, including hypothesis testing, regression analysis, and machine learning. For instance, in machine learning, the assumption of independence among variables (also known as features) is often made to simplify models and make them computationally feasible. However, this assumption may not always hold in real-world data, leading to potential issues in model performance. Therefore, checking for statistical independence is an important step in the data preprocessing stage.
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