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Formal Decision-Making Framework for Error Control in Data-Driven Regulatory Testing

This framework presents a structured approach for making decisions based on observed data while minimizing the risk of errors. It is particularly relevant in the context of regulatory testing, where accurate decision-making is crucial. The concepts of hypothesis testing and binomial distribution are essential in various fields, including operations management and finance, particularly when testing the Capital Asset Pricing Model (CAPM) and options in futures markets. This guide aims to enhance understanding and application of these statistical methods in diverse decision-making scenarios.

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Formal Decision-Making Framework for Error Control in Data-Driven Regulatory Testing

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