One thing I stress with hypothesis testing is understanding the implications of both Type I and Type II errors. These errors can have serious business consequences. A Type I errorrejecting a true null hypothesismight lead to discarding a successful campaign, while a Type II errorfailing to reject a false nullmight result in continuing an ineffective policy. When designing tests, it's crucial to consider which error carries more risk. I once worked on a pricing strategy test where we prioritized avoiding Type I errors because making the wrong call on price could result in significant revenue loss. Think about how you'd approach these risks in your industrydoes one type of error carry more weight