Advanced Statistical Test Accuracy Calculator

Test Parameter Inputs

Core Contingency Matrix

True Positives (TP)

Example: 85

True Negatives (TN)

Example: 920

False Positives (FP)

Example: 15

False Negatives (FN)

Example: 10

Statistical Parameters

Confidence Interval Level (%)

Select statistical significance threshold.

Probability Adjustment Method

Output Data Format

Economic & Risk Weighting

Cost per False Positive ($)

Example: 10.00

Cost per False Negative ($)

Example: 50.00

Decision Threshold Cutoff

Comprehensive Guide to Statistical Test Accuracy

Evaluating diagnostic, screening, or predictive binary tests requires robust statistical measurements derived from a standard confusion matrix. This advanced utility computes essential performance indicators to assess test reliability across medical, machine learning, and industrial quality assurance applications.

Formulas Used

Sensitivity (True Positive Rate): Calculated as $\frac{TP}{TP + FN}$, representing the proportion of actual positives correctly identified.
Specificity (True Negative Rate): Calculated as $\frac{TN}{TN + FP}$, indicating the proportion of actual negatives correctly classified.
Accuracy: Overall correctness computed via $\frac{TP + TN}{TP + TN + FP + FN}$.
Positive Predictive Value (PPV): Defined as $\frac{TP}{TP + FP}$, showing the probability that a positive test result is correct.
Youden's J Statistic: Measures informedness using $Sensitivity + Specificity - 1$.

How to Use This Calculator

Input your absolute frequencies for True Positives, True Negatives, False Positives, and False Negatives into the first column.
Adjust secondary parameters such as confidence intervals and economic cost metrics in the second and third columns.
Click the "Calculate Accuracy Metrics" button to generate immediate diagnostic feedback above the configuration form.

Frequently Asked Questions

What is the difference between sensitivity and specificity? Sensitivity measures a test's ability to correctly identify true positives, while specificity measures its ability to correctly identify true negatives.

Why is economic weighting included? Economic weighting allows analysts to weigh the operational cost penalties associated with false positive errors versus severe false negative oversights.