The article emphasizes the importance of the Poisson distribution in modeling count data, which often defies the assumptions of the normal distribution. It discusses how misapplying the normal distribution can lead to unrealistic predictions, like negative or fractional counts. The author advocates for a deeper understanding of the Poisson distribution, including its mathematical foundations, assumptions, and practical applications in various scenarios. By correctly implementing the Poisson model, data analysts can provide better insights to stakeholders and make sounder decisions, particularly in cases where data generation processes are involved.
The Poisson distribution, when applied correctly, provides not only more meaningful insights for stakeholders but markedly improves model accuracy, inference, and decision-making.
Count data often behaves differently than expected with normal distributions, leading to nonsensical predictions such as negative clicks, underscoring the importance of proper modelling.
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