Type 1 and 2 Errors

In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a “false positive”), while a type II error is incorrectly retaining a false null hypothesis (a “false negative”). The more you try and avoid a Type I error (false positive), the more likely a Type II error (false negative) may happen. Researchers have found that an alpha level of 5% is a good balance between these two issues

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