As the oil price and the Australian cricket team have both declined in recent times, it's time we updated that chart.
And unfortunately, as you can see from the graph below, we can't blame the cricketers for the price of oil (or the economic recession as seen in the Moir cartoon to the right).
Correlations between data sets can occur for 3 reasons:
- There is a direct cause and effect relationship between the two sets - for example, if its rains a lot in one week, then umbrella sales go up - the level of rainfall has caused an increase in umbrella sales;
- There is an underlying reason for the two data sets to move together, as opposed to one causing the other - for example, the heavy rain has also caused more road accidents - umbrella sales and road accidents may look correlated, but one is not causing the other. In some cases you would need to look through a few degrees to find the underlying cause;
- There is no cause and effect and no underlying reason for the correlation - it's simply a coincidence or the work of a devious statistician, as we have here. Scales and time periods are also often changed to make it look like there is a correlation.