Type I and type II errors
Example

Since in a real experiment it is impossible to avoid all type I and type II errors, it is important to consider the amount of risk one is willing to take to falsely reject H or accept H. The solution to this question would be to report the p-value or significance level α of the statistic. For example, if the p-value of a test statistic result is estimated at 0.0596, then there is a probability of 5.96% that we falsely reject H. Or, if we say, the statistic is performed at level α, like 0.05, then we allow to falsely reject H at 5%. A significance level α of 0.05 is relatively common, but there is no general rule that fits all scenarios.
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