Accuracy Calculator
Calculate statistical accuracy using multiple methods: confusion matrix, prevalence, and percent error
Calculate Statistical Accuracy
Choose the appropriate method for your calculation needs
Confusion Matrix Values
Correct positive predictions
Correct negative predictions
Incorrect positive predictions
Incorrect negative predictions
Accuracy Results
Formula used: Accuracy = (TP + TN) / (TP + TN + FP + FN) × 100
Total samples: 0
Accuracy Interpretation
Example Calculation
Medical Diagnostic Test Example
Test results from 1000 patients:
• True Positive (TP): 85 (correctly identified as having disease)
• True Negative (TN): 895 (correctly identified as healthy)
• False Positive (FP): 15 (incorrectly identified as having disease)
• False Negative (FN): 5 (incorrectly identified as healthy)
Calculation
Accuracy = (TP + TN) / (TP + TN + FP + FN) × 100
Accuracy = (85 + 895) / (85 + 895 + 15 + 5) × 100
Accuracy = 980 / 1000 × 100
Accuracy = 98.0%
Confusion Matrix
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0
0
0
When to Use Each Method
Standard Method
When you have actual test results
Uses confusion matrix values
Prevalence Method
When prevalence differs from sample
Adjusts for population prevalence
Percent Error
For measurement accuracy
Compares observed vs true values
Accuracy Tips
Higher accuracy doesn't always mean better performance
Consider precision and recall for imbalanced datasets
F1 score balances precision and recall
Accuracy can be misleading with rare events
Understanding Statistical Accuracy
What is Statistical Accuracy?
Statistical accuracy measures how close a prediction, measurement, or test result is to the true or accepted value. It's a fundamental metric in statistics, machine learning, and scientific measurements.
Types of Accuracy
- •Classification Accuracy: Proportion of correct predictions
- •Measurement Accuracy: Closeness to true value
- •Diagnostic Accuracy: Test performance in medical diagnosis
Accuracy vs. Precision
Accuracy: How close to the true value
Precision: How close repeated measurements are to each other
Key Formulas
Standard: (TP + TN) / (TP + TN + FP + FN)
Prevalence: (Sensitivity × Prevalence) + (Specificity × (1 - Prevalence))
Percent Error: 100% - |Observed - Accepted| / |Accepted| × 100