Colin Cotter

The UK Met Office has been making use of the remains of Hurricane Bertha to publicise the probabilistic aspects of their forecast. In particular, they have been publishing probabilities for various tracks of the storm across the UK.

Why is the forecast being made in terms of probabilities?

The classical idea of a forecast is a prediction of the precise value of something (e.g., temperature) at a particular point in space and time. This is referred to as a deterministic forecast. In a probabilistic forecast, this is expressed as a probability instead. For example, a forecast could say that there is a 60% chance that the temperature will fall between 21 and 25 degrees Celsius, a 20% chance that it will be below this range, and a 20% chance that it will be above.

Read Why are there probabilities in the forecast and why do they keep changing? in full

I’m mainly posting this because I’m getting tired of explaining it repeatedly! There are plenty of other better written articles about this topic but they don’t make the combination of points that I would like to make. This post is about correlation, and what you can and can’t use it for. More generally it is about being careful about drawing conclusions from data.

It is very easy to jump to conclusions when we see changes in the world around us. For example, you might be looking at the success of a particular vaccine in protecting populations from a particular disease. Let’s say that in the countries where the population is given this vaccine, the disease levels are lower.

Read Correlation, causation. Again. in full