lesson
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Imagine stepping on a digital bathroom scale three times in one minute: it reads 145.2ย lb, 145.2ย lb, and 145.2ย lb. You might feel confident in that number, but what if your actual mass is 138.0ย lb and the scale is simply miscalibrated?
In science, getting the exact same number repeatedly is not the same thing as getting the correct answer. To judge how trustworthy data really is, we separate measurements into two distinct qualities: accuracy and precision.
Defining Accuracy and Precision
Accuracy measures how close a measured value is to the true value, which is the universally accepted, real physical quantity of what you are measuring.
Precision measures how close repeated measurements are to one another, regardless of whether they hit the true value. High precision means your individual trials have very little spread.
๐A 2x2 grid comparing Accuracy and Precision using dartboard bullseye targets. Target 1 (top-left): High Accuracy, High Precision (all red darts tightly grouped in the center bullseye). Target 2 (top-right): Low Accuracy, High Precision (all red darts tightly clustered together, but far off in the top-right corner). Target 3 (bottom-left): High Accuracy, Low Precision (darts scattered widely around the board, but their average center is the bullseye). Target 4 (bottom-right): Low Accuracy, Low Precision (darts widely scattered all on the outer edges). Each target card has a bold title, short subtitle, and color-coded status badges (green for high, orange/red for low). Clean modern flat design, dark navy text #1e2945, soft card backgrounds #ffffff with subtle border #e2e8f0.
What actually causes our data to be precise but inaccurate, or accurate but scattered?
Sources of Error: Random vs. Systematic
A systematic error causes measurements to differ from the true value by a consistent amount in the same direction every single time. A classic example is a zero error, where a balance reads 0.5ย g before anything is placed on it, shifting every reading up by 0.5ย g.
A random error causes unpredictable variations in measurements due to uncontrollable factors like human reaction time, air currents, or temperature fluctuations. Random errors spread your results out, reducing precision.
๐A visual diagram showing a horizontal number line with a target 'True Value' marked at 100.0. Diagram shows two comparative scenarios: Scenario A (Systematic Error) shows three measurement dots tightly clustered at 104.1, 104.2, 104.2 with an arrow indicating 'Consistent shift (+4.2) caused by zero error'. Scenario B (Random Error) shows dots scattered at 98.2, 100.1, 101.9 with an arrow indicating 'Unpredictable spread around true value'. Clear labels, color-coded markers (purple for systematic, teal for random), responsive card layout.
How do these errors show up when evaluating real experimental numbers on an exam?