Not every measurement made is exact and there is always an uncertainty with each one. The degree of uncertainty may be high or low due to different experimental errors, but no measurement can be 100% certain.
Random Errors
– Unpredictable errors due to limitations on our ability to make physical measurements.
– Measurements may be too large or too small.
– Cannot be predicted or estimated.
– Most of the time, can be fixed by repeating the experiment or averaging results
Examples: Using a stopwatch to time, change in room temperature
Systematic Errors
– Errors that arise from the experimental set-up.
– Errors are consistent, always too large or too small.
– Can be discovered and possibly avoided and corrected.
Examples: Equipment may not be properly calibrated, temperature not controlled or specified, etc.
Gross Error
– Also called human error, these errors arise from mistakes from the experimenter, like laziness, carelessness or ineptitude.
– Can be reduced by experience, practice and care.
– Mostly hard to correct, and the experiment would need to be repeated.
Examples: Spilling solutions, not following the procedure correctly, not using equipment properly, etc.
Accuracy vs. Precision
Accuracy is how close a measurement is to the real ‘true’ value. For example, a measured value of 3.1 cm is close to the ‘true’ value of 3 cm.
Precision is how close measurements are to each other. For example, measured values of 5.5, 5.6 and 5.7 cm are precise.
It is important to know that you can have data that is precise, but not accurate and vice versa. Data can also be both accurate and precise, or, neither precise nor accurate.
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This article was written for you by Samantha, one of the tutors with SchoolTutoring Academy.