 Often when you're trying to find something out, you can't make a direct measurement of the exact thing you want to calculate. But what you can do is a bunch of measurements that you can then combine into the thing you're actually interested in. So when you have uncertainties in the things that you measure, how do those uncertainties combine to give you an uncertainty in the final result that you're actually interested in? So suppose there's some quantity you want to know about. But it's a function of some other variable, which is the variable that you can measure. So the thing you can measure is called the raw data. And the thing you're trying to calculate is called the derived quantity. So my derived quantity is going to depend on the variable that I'm getting my raw data for. So supposing I measure a particular value for x, so I've got a particular value for that, of course I'm going to have some sort of uncertainty in that. So actually it's going to be some kind of bound there, and it's going to be somewhere in here. And this distance here is the delta x, our uncertainty in x. Now if we project that up to what y is going to be, obviously what we'd do is we'd say, well, that's the value of x that we measured. So this here must be the value of y that we derive. So because of our uncertainty in our raw data in x, we're also going to have some kind of uncertainty for our derived value of y. So just from this picture we can see that this here must be our uncertainty in y in our derived quantity, and you can in fact derive that pictorially using graphing if you like. A more advanced way of doing exactly that process is to use calculus, but we're just going to work out a few rules of thumb for some very simple formulae that come up all the time.