Here's a breakdown:
* Precise measurements are consistent and cluster closely together. Even if they're not accurate (meaning they're not close to the true value), they show a high level of precision.
* Accurate measurements are close to the true value. However, they might not be precise.
Think of it like shooting arrows at a target:
* Precise: All arrows are close together, but they might be clustered away from the bullseye.
* Accurate: All arrows are close to the bullseye, but they might be scattered around it.
* Both precise and accurate: All arrows are clustered close to the bullseye.
Examples:
* Precise: If you weigh a sample five times and get 10.1g, 10.2g, 10.1g, 10.0g, and 10.2g, that's a high level of precision.
* Accurate: If you weigh a sample five times and get 9.9g, 10.1g, 10.0g, 10.2g, and 9.8g, that's a high level of accuracy.
Important note: Precision is often measured using standard deviation, which quantifies the spread of data points around the average.
In scientific research, both precision and accuracy are crucial for reliable and meaningful results.