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Percent Deviation Calculator

Percent Deviation Formula:

\[ PD = \frac{|experimental - theoretical|}{theoretical} \times 100 \]

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1. What is Percent Deviation?

Percent Deviation (PD) is a measure of how much an experimental value differs from a theoretical or accepted value, expressed as a percentage of the theoretical value. It's commonly used in scientific experiments and quality control to assess accuracy.

2. How Does the Calculator Work?

The calculator uses the Percent Deviation formula:

\[ PD = \frac{|experimental - theoretical|}{theoretical} \times 100 \]

Where:

Explanation: The absolute difference between experimental and theoretical values is divided by the theoretical value and multiplied by 100 to get a percentage.

3. Importance of Percent Deviation

Details: Percent deviation is crucial for assessing the accuracy of experimental results, comparing measurements to standards, and evaluating the reliability of data in scientific research and industrial applications.

4. Using the Calculator

Tips: Enter both experimental and theoretical values. The theoretical value cannot be zero (division by zero is undefined). Values can be positive or negative but must be numerical.

5. Frequently Asked Questions (FAQ)

Q1: What's a good percent deviation value?
A: This depends on the field, but generally, lower values indicate better agreement. <5% is often considered good in many scientific fields.

Q2: How is percent deviation different from percent error?
A: They're essentially the same when comparing experimental to theoretical values. Some fields may use the terms interchangeably.

Q3: Can percent deviation be negative?
A: No, because the calculation uses absolute value. The result is always positive or zero.

Q4: What if my theoretical value is zero?
A: Percent deviation is undefined when the theoretical value is zero. Consider using absolute difference instead.

Q5: How should I interpret a high percent deviation?
A: High PD suggests significant difference between experimental and theoretical values, which may indicate measurement errors, flawed methodology, or that the theoretical model needs revision.

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