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Could you differentiate between false positive and false negative in the context of statistical hypothesis testing? And could you please explain their implications?

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Answers

Anonymous

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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 ).
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Interview question asked to Machine Learning Engineers interviewing at OpenDoor, PayPal, Benchling and others: Could you differentiate between false positive and false negative in the context of statistical hypothesis testing? And could you please explain their implications?.