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In data science, understanding different types of probability distributions is crucial, as not all data follows a normal distribution. Here, we'll explore scenarios where a distribution deviates from the normal distribution and provide relevant examples.
Understanding non-normal distributions is essential for accurately modeling real-world data. By recognizing the characteristics and appropriate applications of different distributions, data scientists can choose the right models for their data, leading to better insights and predictions.