Analysing results: anomalies and correlation

In a nutshell

When analysing results it's important to identify anomalies. Data can also be analysed visually using scatter graphs to see relationships between variables. However, correlation between two variables doesn't always mean there is a causal link.

Anomalies

Anomalous results are results which do not fit the trend seen with other values and are likely to be incorrect. These wrong values are likely to be due to random errors such as misreading apparatus. It is important to analyse your results and ask yourself if they make sense. Due to errors, anomalies are not included in any statistical analysis or when drawing lines of best fit.

Correlations

The relationship between the independent and dependent variables can be shown using scatter graphs which show the relationship between two variables.

Type of correlation	Graph	Description
Positive correlation		This is when one variable increases as the other increases.
Negative correlation		This is when one variable increases and the other decreases.
No correlation		This is when there is no relationship between two variables.

Correlation vs. cause

It is important to not jump to conclusions when assessing relationships. A correlation between two variables doesn't always mean a causal link. In experiments where all variables cannot be tightly controlled, such as experiments in nature, the change observed could be caused by a third variable.