Accuracy and precision are two important concepts in science. Both terms apply to any experimental measurement you make. But what do they actually mean? What is the difference between them and which is more important?
Key Takeaways: Accuracy and Precision
- Accuracy: How close a measurement is to the true or accepted value.
- Precision: How close repeated measurements are to each other.
- High accuracy ≠ high precision, and vice versa.
- Best measurements maximize both accuracy and precision.
- Errors, tools, and technique affect both concepts differently.
What Is Accuracy?
Accuracy is a measure of how closely your experimental measurements agree with known values. The closer your measurements are to the known value, the more accurate the measurement.
If you think of the known value as the bullseye on a target, accurate shots (or measurements) mean the average of your shots is at or near the bullseye. The shots in this picture are accurate, but they are all over the target. This brings up the concept of precision.
Examples of Accuracy in Science
Accuracy refers to how close a measurement is to the true or accepted value. Here are a few examples where accuracy is critical:
- Chemistry: A titration to determine the concentration of an acid is accurate when the measured result matches the known concentration within experimental uncertainty.
- Physics: Measuring the acceleration due to gravity with a pendulum experiment is accurate if your value is very close to 9.80665 m/s².
- Medicine: A blood glucose test that reads 90 mg/dL when the patient’s actual glucose level is 90 mg/dL demonstrates high accuracy.
- Astronomy: Determining the distance to a nearby star using parallax is accurate if the result agrees with independent, well-established methods.
- Engineering: A bridge component designed to be 5.000 m long is accurate if the manufactured part measures exactly 5.000 m within tolerance limits.
In each case, accuracy depends on correct calibration, reliable methods, and minimizing systematic error.
What Is Precision?
Precision is a measure of how close your experimental measurements agree with each other. The closer each measurement is to the other measurements, the more precise your measurement.
If the measurements were shots in a target, the closer the holes are to each other, the more precise the shots. Here, the shots are all clustered together. They would be considered a precise collection of shots. The only problem is they are not very accurate.
Examples of Precision in Science
Precision refers to how close repeated measurements are to one another, regardless of whether they are correct. Here are a few examples:
- Biology: If you are measuring plant growth rates and three measurements all show 4.20 cm, the results are precise even if the actual growth was 4.50 cm.
- Meteorology: Recording daily temperatures with a weather station is precise if all readings are within 0.1 °C of each other.
- Materials Science: Weighing a sample on an analytical balance that consistently returns 2.351 g, 2.352 g, and 2.351 g demonstrates high precision.
- Environmental Science: Testing water samples for lead is precise if every test reports 3.02 ppm, even if calibration is slightly off.
- Astronomy: Repeatedly timing an exoplanet transit with results within one second of each other demonstrates precision, though not necessarily accuracy if the instrument has drift.
Precision is strongly affected by random error, instrument stability, and consistency in experimental technique.
How to Control Accuracy and Precision
Both accuracy and precision are the goal of any measurement. Variations in accuracy and precision are largely controllable.
In target shooting, you improve accuracy by moving closer to the target, or using an aiming aid like a scope or laser pointer. You improve precision by mounting your gun to a table or bench or shooting indoors out of the wind.
In a lab, improve accuracy and precision by using the same procedure taking each measurement and using tools designed for measurements. For example, you get a more accurate and precise volume measurement using a graduated cylinder than you do using a water glass.
In general, use good tools designed for the measurement you are trying to make and pay close attention to what you are doing when you use them. This helps any measurement you make.
Which Is More Important? Accuracy or Precision?
Accuracy is more important when trying to hit a target. You either hit your target or you don’t. It does no good to be precise if you miss all your shots. You can improve accuracy in future measurements by factoring in a correction factor. If your shots are all going to the left, alter your aim to the right. Accuracy is something you can fix in future measurements.
Precision is more important in calculations. When using a measured value in a calculation, you can only be as precise as your least precise measurement. This is the main idea behind the topic of significant figures in calculations. You can improve precision by using a better measuring tool and/or improving your skill at using the tool.
Accuracy and precision are both important to good measurements in science. Try to make the best of both in all your measurements. But, no matter how hard you try, measurements always deviate slightly from true values. This is because of error.
| Aspect | Accuracy | Precision |
|---|---|---|
| Definition | Closeness to the true value | Closeness of repeated measurements |
| Example | Hitting the bullseye | All darts clustered together |
| Affected by | Systematic error | Random error |
| Fixed by | Calibration or correction | Better technique/tools |
| Key in | Reporting correct results | Ensuring reproducibility |
Accuracy & Precision vs. Measurement Error
In scientific measurement, error is the deviation between a measured value and its true value. Error is a natural and unavoidable part of any experiment. Understanding how error relates to accuracy and precision can help readers grasp why measurements vary and how to improve them.
- Random error stems from unpredictable fluctuations, such as environmental variations, instrument sensitivity, or slight procedural inconsistencies. It primarily affects precision, reflected in how tightly repeated measurements cluster together. Averaging multiple readings helps reduce its impact
- Systematic error arises from consistent flaws like miscalibrated instruments, biases in procedures, or flawed theoretical assumptions. This type of error skews all measurements in the same direction, degrading accuracy even if the results are consistently reproducible.
In essence:
- Precision is influenced by random error (repeatability),
- Accuracy is affected by systematic error (correctness),
- Best measurements minimize both errors to be both accurate and precise.
References and Further Reading
- Barford, N. C. (1985). Experimental Measurements: Precision, Error and Truth (2nd ed.). Wiley. ISBN: 978‑0471907015.
- Rabinovich, S. G. (2017). Evaluating Measurement Accuracy: A Practical Approach (3rd ed.). Springer. ISBN: 978‑3‑319‑60124‑3. doi:10.1007/978‑3‑319‑60125‑0.
- Rofatto, V. F.; Matsuoka, M. T.; Klein, I. (2025). “Precision, Trueness and Accuracy: On the Errors and Uncertainties”. Revista Brasileira de Cartografia 77(1). doi:10.14393/rbcv77n0a-74914
- van Smeden, M.; Lash, T.L.; Groenwold, R.H.H. (2020). “Reflection on modern methods: five myths about measurement error in epidemiological research”. Int J Epidemiol. 49(1): 338-347. doi:10.1093/ije/dyz251