What are the meanings of false positives and false negatives in statistical hypothesis testing, and how do they affect the eventual outcomes?

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Anonymous · Accepted Answer

False positives: This often referred to known as Type 1 error .This indicates
that my formulated null hypothesis (H0) is true but I reject it( My decision
about estimation of population parameters are correct but I can't accept
(reject) .In contrast Type 2 error also known as false negatives this represent
my null hypothesis is false but I failed to reject it ,(I don't have any strong
enough evidence to the reject this ).

What are the meanings of false positives and false negatives in statistical hypothesis testing, and how do they affect the eventual outcomes?

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