Drawing conclusions and evaluations
In a nutshell
Evaluating your data is important to ensure your results are valid and your conclusions can be supported. Looking at correlation and percentage error are ways to evaluate your data.
Drawing conclusions
When analysing your data, you need to make sure your conclusions are valid. They can only be considered valid if they answer the original question that you set out to investigate. One way to draw conclusions is to look at the correlation between two variables.
Relationship | Description | Example |
Positive correlation | When 'A' increases, 'B' also increases. | |
Negative correlation | When 'A' increases, 'B' decreases. | |
No correlation | There is no relationship between 'A' and 'B'. | |
Note: Correlation does not equal causation. Just because it appears that there is a relationship between two variables, it does not mean that one is causing a change in the other.
Uncertainty
Definition
Uncertainty is the amount of error your measurements might have. There is always a degree of uncertainty in your measurements because the equipment and apparatus have a limited sensitivity.
Example
A measuring cylinder may measure to the nearest 1 cm3 but the real volume may be 0.5 cm3 higher or lower. The measuring cylinder has an uncertainty value of ±0.5 cm3, this is called the margin of error.
Calculating percentage error
Percentage error can be calculated using this formula:
percentage error=readinguncertainty×100
Example
Calculate the percentage error of 15 cm3 of water, with an uncertainty value of 0.01 cm3.
Input the values into the formula:
percentage error=150.01×100
Simplify:
percentage error=0.00067×100=0.067%
Therefore, the percentage error is 0.067%.
It is possible to minimise errors in your measurements by using the most sensitive equipment you have available.
Evaluation
In your evaluation, you need to assess the repeatability, reproducibility and validity of your experiment and data. You also need to evaluate your method and explain whether your results confidently back up your conclusion.
There are some important questions you should answer when evaluating.
- Did you do enough repeats?
- Do you think you'd get similar results if you were to repeat this experiment? Why?
- How does your data compare to existing data?
- Do other people make the same conclusions from your results?
- Does your data answer the question you set out to answer?
- How could you make your results more reliable and accurate?
- What were the limitations in your method?
- What were the sources of error in your experiment?
- How could you improve the experiment if you were to do it again?