Analysis

What is 'Analysis'?

Analysis effectively assesses data processing. It concerns the extent to which the report provides evidence that the data selected and recorded have been processed and interpreted in ways that are directly relevant to the research question and provide the basis for a conclusion.

Before you can address this criterion fully you need to understand the importance of the correct use of significant figures. You also need to understand the difference between error and uncertainty and how the total uncertainty associated with a result obtained from using several different pieces of apparatus can be calculated. This information is provided in the two linked pages Significant figures and Error & uncertainty.

Achieving the maximum mark

To score the maximum of six marks for Analysis in your Individual Scientific Investigation you need to:

  • include sufficient relevant quantitative and qualitative raw data that is able to support a detailed and valid conclusion to the research question.
  • show that the recorded data has been sufficiently processed in an appropriate and accurate manner so that a conclusion can be drawn which is fully consistent with the experimental data.
  • show clearly the impact of the uncertainties associated with measured data on the processed results and how they affect the conclusion.

The following three areas are particularly important when it comes to assessing 'Analysis'.

1. Quantitative and qualitative data

All raw quantitative data should be recorded, usually in an appropriate tabulated form, together with the associated uncertainties. The correct units should be clearly shown (often by the heading at the top of each column) and the data expressed consistently to the correct number of significant figures. Relevant qualitative data (change in colour, smell etc.) should also be recorded. If you are using a data logger the raw data should be given in tabular form in addition to any graph produced directly by the data logger. Ensure that the data is relevant and that sufficient readings have been recorded to produce a conclusion with scientific validity that directly relates to your research question.

2. Processing of data

The raw data needs to be processed accurately and in an appropriate way. This can take a variety of forms. If it is graphical then a suitable graph should be plotted ideally to give a straight line of best fit, if appropriate. Some of the data may be manipulated beforehand to produce a straight line (e.g. by taking the inverse, squaring or taking the logarithmic form of one of the variables etc.). Alternatively the raw data may be plotted in the form that it was measured and then the graph manipulated by, for example, taking the gradient or interpolating or extrapolating to find a new value. If the processing is non-graphical then it is important that any equation used is properly derived and not just taken from material published elsewhere.

3. Overall uncertainty

Each measurement is associated with an uncertainty. When the data is processed the total uncertainty inherent in the calculated result(s) should be determined, usually by summing up all the percentage uncertainties for each measured value to give the total percentage uncertainty. You should also list any assumptions you have made which can affect the validity of your result. For example, how pure were all the chemicals involved, did the reaction go to completion, were other possible reactions also taking place which might affect the final result, did the solution have the same specific heat capacity as pure water etc. etc. etc.?

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