Experiment!: Planning, Implementing and Interpreting

7.1 The Role of Statistics in Data Analysis

Anyone who has tried to choose one cookie from a tray that comes fresh from the oven knows how difficult it can be. Some cookies are bigger than others, some have a nicer color and some have more raisins in them. We are all vaguely aware that this sort of variation exists everywhere in life. The distance we drive on a tank of fuel varies from time to time, we don't arrive at work at the same time every morning, our hair does not look the same every day – everything varies.

In many cases we do not bother to cope with this variation. If the fuel tank is empty we just fill it up, regardless of the distance we have driven. In other cases we need to take the variation into consideration. If we are required to be at work at a certain time, for example, we probably get up a little earlier in the morning to allow for some unforeseen delays along the way. If we are analyzing data from an experiment it is often necessary to account for the variation in much greater detail. The variation in such data comes both from the measurement setup and the process that we are studying. It is crucially important to find ways to cope with this variation in order to draw conclusions that are correct. For example, if the experiment is aimed at detecting a subtle effect it is important to know how much of the variation comes from the measurement setup. If that variation is larger than the one we create by our deliberate experimentation, something must be done to ...