Calculator Form
Enter confusion matrix values for a chemistry assay, screening panel, or laboratory method validation review.
Example Data Table
| Example Batch | TP | FN | TN | FP | Sensitivity | Specificity |
|---|---|---|---|---|---|---|
| Metal Ion Screen | 45 | 5 | 88 | 12 | 90.00% | 88.00% |
| Contaminant Assay | 78 | 9 | 130 | 11 | 89.66% | 92.20% |
| Biochemical Marker Panel | 64 | 16 | 144 | 8 | 80.00% | 94.74% |
Formula Used
Sensitivity = TP / (TP + FN)
Specificity = TN / (TN + FP)
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Positive Predictive Value = TP / (TP + FP)
Negative Predictive Value = TN / (TN + FN)
Youden's J = Sensitivity + Specificity - 1
Likelihood Ratios = Sensitivity / (1 - Specificity) and (1 - Sensitivity) / Specificity
These formulas help evaluate laboratory screening quality, assay discrimination, and false classification risk. In chemistry workflows, they are useful when comparing a test method against a reference standard.
How to Use This Calculator
- Enter the assay or batch label to identify the result set.
- Fill in true positives, false negatives, true negatives, and false positives from your laboratory confusion matrix.
- Add an optional prevalence value if you want adjusted PPV and NPV estimates using Bayes-style interpretation.
- Click Calculate to display results above the form, review the graph, and inspect detailed metrics.
- Use Download CSV for tabular reporting and Download PDF for printable summaries.
- Compare sensitivity and specificity together before deciding whether a screening method is suitable for confirmation, triage, or rejection workflows.
FAQs
1. What does sensitivity mean in a chemistry test?
Sensitivity measures how often the method correctly identifies true positive samples. High sensitivity reduces missed detections when contaminants, markers, or target analytes are actually present.
2. What does specificity mean?
Specificity measures how often the method correctly labels true negative samples. High specificity reduces false alarms and helps avoid unnecessary confirmations, retesting, or product rejection.
3. Why should sensitivity and specificity be reviewed together?
A method can be very sensitive yet produce many false positives, or highly specific while missing positives. Reviewing both shows the trade-off between detection strength and false classification control.
4. What is a true positive in this tool?
A true positive is a sample that truly contains the target condition and is correctly reported as positive by the assay, instrument rule, or screening method.
5. What is the purpose of predictive values?
Positive and negative predictive values estimate how trustworthy positive and negative results are in practice. They are especially useful when prevalence changes across products, sites, or sample populations.
6. When should I use prevalence override?
Use prevalence override when the study sample mix differs from real-world production or field conditions. It helps estimate adjusted predictive values for expected operating environments.
7. Is a higher accuracy always better?
Not always. Accuracy can look strong in imbalanced datasets with many negatives. Sensitivity, specificity, and predictive values often provide a clearer picture of assay quality.
8. Can this tool support method validation reports?
Yes. It provides core classification metrics, a quick visual chart, and export options that can support draft validation summaries, internal reviews, and comparison notes.