All measurements have an inherent uncertainty or error associated. It simply is not possible to measure any value with 100% certainty, either due to the limitations of the measuring device, or to our own human limitations.
Good scientific practise recognises these limitations and attempts to quantify experimental results, giving a clearer indication of the reliability and precision involved in any procedure. This chapter examines the nature and recording of these inherent uncertainties.
Essential idea: All measurement has a limit of precision and accuracy, and this must be taken into account when evaluating experimental results.
Qualitative data includes all non-numerical information obtained from observations not from measurement
Quantitative data are obtained from measurements, and are always associated with random errors/uncertainties, determined by the apparatus, and by human limitations such as reaction times.
Propagation of random errors in data processing shows the impact of the uncertainties on the final result.
Experimental design and procedure usually lead to systematic errors in measurement, which cause a deviation in a particular direction.
Repeat trials and measurements will reduce random errors, but not systematic errors.
Applications and skills
Distinction between random errors and systematic errors.
Record uncertainties in all measurements as a range (±) to an appropriate precision.
Discussion of ways to reduce uncertainties in an experiment.
Propagation of uncertainties in processed data, including the use of percentage uncertainties.
Discussion of systematic errors in all experimental work, their impact on the results and how they can be reduced.
Estimation of whether a particular source of error is likely to have a major or minor effect on the final result.
Calculation of percentage error when the experimental result can be compared with a theoretical or accepted result.
Distinction between accuracy and precision in evaluating results.