I’m trying to solve a problem involving percent error, but I forgot the formula. Can someone explain the percent error formula in simple terms? Thank you for your help!
Percent error formula? Easy peasy:
% Error = |(Actual Value - Measured Value) / Actual Value| × 100
You subtract the measured value from the actual value, take the absolute value 'cause negativity doesn’t matter here, divide by the actual value (gotta keep it real), and lastly, multiply by 100 to slap a percentage label on it.
That’s it. Don’t overthink it—math doesn’t bite… usually.
Alright, so @techchizkid gave you the formula in a pretty chill (and accurate) way. I do think they over-simplified it a bit though—because there’s one thing that really trips people up: the actual value and measured value positions.
Let me put it this way: people sometimes confuse which one’s the “Actual Value” and the “Measured Value.” Actual value is like the “truth” or what’s supposed to happen (e.g., the boiling point of water at 100°C). Measured value is what you observed, your result, and let’s be honest, what’s probably a bit wrong. So, if you mix that up? Yeah, your % error is completely off. 
Also, about that absolute value thing—don’t just gloss over it! Sometimes students leave the result as a negative, but in context, percent error isn’t about being too high or too low. It’s about accuracy, not direction—so don’t skip making it positive. Really, slap those bars (| |) around the subtraction.
If I had to be nitpicky, though, I sometimes find using relative error more practical—it’s basically the same formula without multiplying by 100. Keeps it as a fraction/decimal if you’re skipping the percent vibe, especially in experiments where tiny numbers matter more.
Do you still need an example thrown in here, though? Possible, 'cause understanding’s nothing without context. 
Alright, quick dive into percent error—absolutely essential if precision and accuracy in measurements are your thing. While @espritlibre and @techchizkid gave solid takes, I’ll slide in with a slightly different angle, mainly to keep things fresh and useful. Also, buckle up, 'cause not all percent error problems are as straightforward as the formula might seem!
Let’s dissect the formula a bit more:
% Error = |(Actual Value - Measured Value) / Actual Value| × 100
PROS:
- Mathematical Simplicity: The formula is easy to grasp, even for non-math whizzes.
- Absolute Value is Key: Takes away worries about whether your mistake leaned high or low.
- Applicable Across Fields: From lab experiments to surveys, it’s flexible.
CONS:
- Confuses Beginners Easily: Which one is “Actual” and which is “Measured”? People mess this up a LOT.
- Over-reliance on Absolutes: Percent error ignores ‘overshooting vs undershooting’ issues—it’d be nice if there was some indicator for that.
COMPETITION CORNER
- @techchizkid’s take: Super informal, breaks it down in a ‘math is easy’ tone, but sometimes casual explanations leave subtle details out. Could miss points if you’re new to the concept.
- @espritlibre’s cautionary advice: Totally valid—messing up Actual vs Measured is a classic pitfall, but the additional context around ‘relative error’ is appreciated for advanced users.
COMMON TRAPS TO AVOID:
- Switching Variables: @espritlibre nailed this—Actual is your “ideal” benchmark, while Measured is what your experiment spat out. Screwing those up? Error city.
- Ignoring the Abs Value: Percent error doesn’t care if you’re over or under—just how far off you were. No negative results, alright?
- Applying This Blindly: On rare occasions, relative error may give a better sense of your accuracy, especially if you’re working in a context where decimals make more sense than percentages (e.g., tiny weights).
OKAY, PICTURE THIS:
You’re boiling water. Actual boiling point? 100°C. Measurement? 97°C. Plugging that into our formula:
% Error = |(100 - 97) / 100| × 100 = 3%
Now reverse it—let’s say your thermometer said 105°C. Same setup, just:
% Error = |(100 - 105) / 100| × 100 = 5%
Boom. Whether too low or too high, the absolute value ignores direction—every time. This keeps calculations grounded in ‘how close were we?’ rather than ‘where did we mess up?’
In case anyone wants a debate on when percent error becomes irrelevant, I’d argue it’s when your actual value is ambiguous. Like, measuring something without a universally accepted “truth value”—opinion polls, social experiments, etc. In those cases, this formula’s out, and we drift to confidence intervals or other methods.
So, TL;DR:
Percent error’s formula is fundamental, but understanding where and when to apply it makes all the difference. Want just the basics? Sure, but don’t leave out the nuances if you’re aiming for mastery.