1. What Is Error Percentage?

Error percentage is a measure of the accuracy of a measurement or calculation compared to the true or accepted value. It expresses the difference between measured and true values as a percentage of the true value.

2. How Does The Calculator Work?

The calculator uses the error percentage formula:

Where:

• \( \text{Measured} \) — The value obtained from measurement or calculation
• \( \text{True} \) — The actual or accepted reference value

Explanation: The formula calculates the relative error between measured and true values, then converts it to a percentage for easier interpretation.

3. Importance Of Error Percentage Calculation

Details: Error percentage is crucial in scientific research, quality control, engineering, and data analysis to assess measurement accuracy, validate results, and ensure reliability of experimental data.

4. Using The Calculator

Tips: Enter both measured and true values. The true value cannot be zero (division by zero error). Values can be positive or negative depending on the context.

5. Frequently Asked Questions (FAQ)

Q1: What does a positive error percentage mean?
A: A positive error percentage indicates the measured value is higher than the true value (overestimation), while negative indicates underestimation.

Q2: What is considered an acceptable error percentage?
A: Acceptable error varies by field. In laboratory settings, <5% is often acceptable, while engineering may require <1%. Always check industry standards.

Q3: How is error percentage different from absolute error?
A: Absolute error is the numerical difference (Measured - True), while error percentage expresses this difference relative to the true value as a percentage.

Q4: Can error percentage be greater than 100%?
A: Yes, if the measured value is more than double the true value, error percentage will exceed 100%.

Q5: When should I use error percentage vs other error measures?
A: Use error percentage when comparing errors across different scales or magnitudes. For single measurements, absolute error may be more appropriate.