Explain the conceptual and quantitative relationships between Alpha risk and Beta risk when testing hypotheses, and include the impact sample size plays in managing these risk levels.
Don't just write a brief blurb about the definitions of alpha and beta error. In particular, you should be writing about the ways that accepting an Alpha risk increase can impact total risk -- this is what we concentrate on as engineers. Hypothesis testing is a statistical technique, but risk management is an engineering requirement. Also consider what happens to these distributions as we increase the sample sizes being analyzed (Hint: What happens to the standard error as n increases?
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