Evaluating data

The quality of any data should be evaluated before making any conclusions.

Precision, repeatability and reproducibility

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 measurement. If the repeat measurements are close together, the data is precise and repeatable.


Evaluation of the data should also consider accuracy. A measurement is accurate if it is close to the true value.

To ensure the data is as accurate as possible, work out the best estimate of the true value.

Identify any outliers (anomalous results) in the data. These are results that are very different to the others. For example, 2.2 and 0.1 are outliers:

Try to explain why the outlier is different. An outlier may be removed if there is a good reason to do so. For example, if there is a measurement or recording error.

Find the mean of the remaining results. To find the mean add together the results and divide by the number of measurements.

Sometimes, outliers are not obvious until a graph is plotted.

If a data point does not fit into a trend then it is an outlier. The result should be circled in the graph and labelled as an outlier.

A graph showing some points plotted with a line of best fit, there is an outlier point highlighted with a circle