The quality of any data should be evaluated before making any conclusions.
|Precision||Measurements are in close agreement|
|Repeatable||Measurements are very similar when repeated by the same person or group, using the same equipment and method|
|Reproducible||Measurements are very similar when repeated by a different person or group, using different equipment and/or methods|
Precision and repeatability can be seen easily from a table of results containing repeat measurements. If the repeat measurements are close together, the data is precise and repeatable.
To ensure the data is as accurate as possible, work out the best estimate of the true value:
Using the example above:
Sum of values = 0.9 + 1.0 + 1.2 + 0.8 + 1.0 + 1.1 + 0.8 + 1.2 = 8.0
Number of measurements = 8
Mean = 8.0 ÷ 8 = 1.0
This mean is the best estimate of the true value.
The mean should be given to the same number of significant figures as the measurements in the table.
Data cannot always be relied upon. There can be errors, and all measurements have some level of uncertainty.
Random errors are unpredictable and can be due to human error, eg in judging when to stop a timer.
Systematic errors cause results to differ from the true value by the same amount each time. These could be due to:
To describe the accuracy of an experiment, discuss the level of confidence in the results. If only one reading was taken, then you can be less confident in the accuracy of the results.
The range describes the difference between the highest and lowest repeat results. The smaller the range, the more confident you can be in the accuracy of the result.
Having evaluated the data, suggest improvements to the way in which the experiment was carried out that could improve the quality of the results.