In statistical testing, a Type 2 Error occurs when the null hypothesis is not rejected even though it is false. This error, also known as a false negative, has significant implications in hypothesis testing as it can lead to incorrect conclusions.
In statistical hypothesis testing, a Type I Error occurs when the null hypothesis is rejected when it is actually true. This incorrect rejection leads to a false positive result.
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