How do you calculate sensitivity and specificity?

Sensitivity is true positives divided by all diseased animals tested. Specificity is true negatives divided by all non-diseased animals tested.

Do PPV and NPV change with prevalence?

Yes. Positive and negative predictive values depend on disease prevalence in the tested population, while sensitivity and specificity are usually treated as test performance characteristics.

How should NAVLE-style questions about sensitivity and specificity be approached?

Start by drawing the 2x2 table, then decide whether the question asks down a disease column (sensitivity or specificity) or across a test-result row (PPV or NPV).

Diagnostic reasoning

Sensitivity, specificity & diagnostic test accuracy calculator

Calculate sensitivity, specificity, PPV, NPV, likelihood ratios, and NAVLE-style test interpretation with study prompts for common exam traps.

Sensitivity

-- %

Enter test data

Use a 2x2 table or prevalence model to calculate diagnostic metrics.

Specificity --

True negative rate.

PPV --

Chance a positive is truly diseased.

NPV --

Chance a negative is truly non-diseased.

LR+ --

Positive likelihood ratio.

LR- --

Negative likelihood ratio.

Prevalence --

Disease frequency in tested group.

NAVLE-style interpretation advice

Draw the 2x2 table first, then decide which denominator the question asks for.
Sensitivity and specificity use disease status columns. PPV and NPV use test result rows.
Predictive values change with prevalence; sensitivity and specificity usually do not.

Study coach

Step 1: identify whether the denominator is disease status or test result.
Step 2: use columns for sensitivity/specificity and rows for PPV/NPV.
Step 3: check prevalence before trusting PPV or NPV.

NAVLE study mode

Use this as a short teaching loop: name the denominator, choose the metric, then decide whether prevalence changes the answer.

Sensitivity

Of animals that truly have the disease, how many test positive?

TP / (TP + FN)

Specificity

Of animals that truly do not have the disease, how many test negative?

TN / (TN + FP)

Positive predictive value

Of animals with a positive test, how many truly have the disease?

TP / (TP + FP)

Negative predictive value

Of animals with a negative test, how many truly do not have the disease?

TN / (TN + FN)

"Correctly identifies diseased animals" points to sensitivity.
"Correctly identifies non-diseased animals" points to specificity.
"Given a positive result, what is the chance of disease?" points to PPV.
"Given a negative result, what is the chance disease is absent?" points to NPV.
"Disease is rare/common in this population" is usually a predictive-value trap.

Practice drills

Drill 1: low-prevalence screening

A screening test has 95% sensitivity and 90% specificity, but disease prevalence is 2%. Which result metric becomes the main trap?

Drill 2: disease-present denominator

In diseased animals, 72 test positive and 18 test negative. The question asks for the test's ability to detect disease. Which metric?

Drill 3: high-sensitivity rule-out

A test has 98% sensitivity and 70% specificity. A NAVLE-style question asks what a negative result is best used for.

Drill 4: high-specificity rule-in

A test has 70% sensitivity and 98% specificity. A NAVLE-style question asks what a positive result is best used for.

2x2 table

Test result	Disease present	Disease absent
Positive	TP	FP
Negative	FN	TN

Formula audit trail

Sensitivity: TP / (TP + FN)
Specificity: TN / (TN + FP)
PPV: TP / (TP + FP)
NPV: TN / (TN + FN)

Basis and limits

Scope: arithmetic and interpretation support for educational and clinical reasoning workflows.
Gold standard assumption: the 2x2 table assumes the disease-present and disease-absent columns come from an accepted reference standard.
Predictive values: PPV and NPV depend strongly on prevalence and the population being tested.
Clinical use: no diagnostic test should be interpreted without signalment, history, physical findings, pretest probability, and test limitations.

Related tools

Last reviewed: June 2026

Sources: Merck Veterinary Manual diagnostic test table; Ohio State College of Veterinary Medicine diagnostic testing review; NCBI StatPearls diagnostic testing accuracy overview.

Sensitivity, specificity & diagnostic test accuracy calculator

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