Per:

Disease Prevalence	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The proportion that people with the disease make out of all persons in the sample. \( DP = \frac{TP+FN}{N} \)					DP = > <
Sensitivity	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you test positive if you have the disease. It's the same as the power (1 - β) of the test. \( Sensitivity = \frac{TP}{TP+FN} \)					Sensitivity = > <
Specificity	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you test negative if you don't have the disease. \( Specificity = \frac{TN}{FP+TN} \)					Specificity = > <
Positive Predictive Value	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you have the disease if you test positive. \( PPV = \frac{TP}{TP+FP} \)					PPV = > <
Negative Predictive Value	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you don't have the disease if you test negative. \( NPV = \frac{TN}{FN+TN} \)					NPV = > <
Accuracy	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that the test gives the correct answer (either positive or negative) \( A = \frac{TP+TN}{N} \)					Accuracy = > <
False Positive Rate	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you test positive if you don't have the disease. It's the same as the risk of type 1 error (α). \( FPR = \frac{FP}{FP+TN} \)					FPR = > <
False Negative Rate	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The "miss rate". The probability that you test negative if you have the disease. It's the same as the risk of type 2 error (β). \( FNR = \frac{FN}{TP+FN} \)					FNR = > <
False Discovery Rate	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you don't have the disease if you test positive. \( FDR = \frac{FP}{TP+FP} \)					FDR = > <
False Omission Rate	Interpretation	Value	SE	% Confidence Interval		Null Hypothesis		Z value	P value
	The probability that you have the disease if you test negative. \( FOR = \frac{FN}{FN+TN} \)					FOR = > <

Positive Likelihood Rate	Interpretation	Value
	The probability that a person with the disease tests positive divided by the probability that a person without the disease tests positive. If the rate is greater than 1 the test is more likely to give a true positive than a false positive. If the rate is between 0 and 1 the test is more likely to give a false positive than a true positive. \( LR+ = \frac{sensitivity}{1 - specificity} \)
Negative Likelihood Ratio	Interpretation	Value
	The probability that a person with the disease tests negative divided by the probability that a person without the disease tests negative. If the rate is greater than 1 the test is more likely to give a false negative than a true negative. If the rate is between 0 and 1 the test is more likely to give a true negative than a false negative. \( LR- = \frac{1 - sensitivity}{specificity} \)
Diagnostic Odds Ratio	Interpretation	Value
	The rate between the odds of a person testing positive who has the disease relative to the odds of a person testing positive who do not have the disease. \( DOR = \frac{LR+}{LR-} \)
F1 Score	Interpretation	Value
	The F1-score can be used as a single measure of performance of the test for the positive class. It is a measure of the test's accuracy. $$ F_1 = 2 \times \frac{PPV \times Sensitivity}{PPV + Sensitivity} $$
Power	Interpretation	Value
	The power of the test is the probability that you test positive if you have the disease. It's the same as the sensitivity. \( Power = 1 - \beta = sensitivity \)