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One way is to increase the significance level, therefore reducing the risk of a Type II error. The downside is it also increases the risk of a Type I error.

*Bonus Points*

- The power of a test is the probability of correctly rejecting a false null hypothesis: 1 - Type II error probability.
- If the power of an experiment is low, then there is a good chance that the experiment will be inconclusive and need to redesigned.
- Power is not about whether or not the null hypothesis is true. It is the probability the data gathered in an experiment will be sufficient to reject the null hypothesis. The experimenter asks the question: If the null hypothesis is false with specified population means and standard deviation, what is the probability that the data from the experiment will be sufficient to reject the null hypothesis?

*Category: Quantitative Analysis*

*Category: C++ Quant > Finance*

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