1. What is Mean Bias Error?

Mean Bias Error (MBE) is a statistical metric that measures the average bias in predictions. It indicates whether a model tends to overpredict or underpredict on average.

2. How Does the Calculator Work?

The calculator uses the MBE formula:

Where:

• \( Predicted \) — Predicted values from the model
• \( Observed \) — Actual observed values
• \( n \) — Number of data points

Explanation: The formula calculates the average difference between predicted and observed values. Positive MBE indicates overprediction, negative MBE indicates underprediction.

3. Importance of MBE Calculation

Details: MBE helps identify systematic bias in predictive models. It's crucial for model validation and improvement, especially in fields like forecasting, machine learning, and scientific modeling.

4. Using the Calculator

Tips: Enter predicted and observed values as comma-separated lists. Ensure both lists have the same number of values and are in the same order.

5. Frequently Asked Questions (FAQ)

Q1: What does a positive MBE value mean?
A: Positive MBE indicates the model tends to overpredict (predicted values are higher than observed values on average).

Q2: What does a negative MBE value mean?
A: Negative MBE indicates the model tends to underpredict (predicted values are lower than observed values on average).

Q3: What is the ideal MBE value?
A: The ideal MBE is 0, indicating no systematic bias. However, the acceptable range depends on the specific application and data scale.

Q4: How does MBE differ from MAE?
A: MBE measures bias (direction of error), while MAE (Mean Absolute Error) measures magnitude of error regardless of direction.

Q5: Can MBE be used alone for model evaluation?
A: No, MBE should be used with other metrics like RMSE, MAE, and R² for comprehensive model evaluation.