What are Type1 & Typ2 error?

Data Science, AI and ML
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ML, Error

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A type 1 error is also known as a false positive and occurs when a user incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance. It is also called as “Alpha error”

A type II error is also known as a false negative and occurs when a user fails to reject a null hypothesis which is really false. Here a user concludes there is not a significant effect, when actually there really is. It is also called as “Beta Error”

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Null Hypothesis: You are a medically fit person

Type I Error: You take an HIV test and it comes back positive, but you don’t actually have HIV. (False Positive)

OR, incorrect rejection of a true Null Hypothesis.

Type II Error: You take an HIV test and it comes back negative, but you actually have HIV. (False Negative)

OR, incorrectly retaining a false Null Hypothesis.