Evaluating Results & Drawing Conclusions (OCR AS Physics): Revision Note

Evaluating Results & Drawing Conclusions

• Evaluating experimental results and drawing conclusions from them are two very important skills

• Evaluation of results is a different skill from evaluation of the experimental procedure used to obtain those results

• Conclusions can only be drawn from the results once they have been properly evaluated

  • For example, during the planning of an experiment, potential limitations of the experimental procedure should have already been identified

  • Before drawing conclusions, the impact that these limitations could theoretically have had (or may actually have had) on the data collected should be evaluated

  • If this evaluation shows these potential impacts to be negligible, a conclusion can more likely be drawn from the results

  • If it is decided that the limitations could have had a significant impact on the data, then it is much harder to draw a conclusion and it should be recognised that any conclusions drawn have a greater chance of being incorrect

Identifying Anomalies

• Experimental errors (also known as operator errors or ‘one off’ errors) will affect the results of an experiment and can produce anomalies

  • These anomalies should be identified during the evaluation of results and before drawing conclusions

  • Anomalies can be identified by looking for results or data points on a graph that do not fit with the trend or with other replicates carried out during the experiment

  • These anomalous results will show a larger difference from the mean than the rest of the results (a result is often taken to be anomalous if it differs from the mean result by more than 10%)

• The results or ‘data’ collected from an experiment can be made more reliable if the experiment is repeated several times and anomalies are removed

  • This, in turn, allows more valid conclusions to be drawn

Identifying an anomalous result from a graph

• If an anomaly occurs in the experiment:

  • Ignore this value when calculating the mean

  • Repeat this measurement