- What does variation look like? Well, it depends on what kinda data that we're talking about. To be concrete, let's focus on continuous data and by that, I mean real numbers or numbers measured with decimal points. So, thinking about continuous data, a lot of the data in experimental biology follows a normal or a bell curve shape distribution. By that, I mean that if these are the values that you can observe, there is some typical value that we'd denote with a Greek letter or the mean that is often observed. But variability refers to the fact that when you make a measurement of this, there may be some values that are either higher or lower than the mean. This axis in the plot is showing how likely it is to get these different observations. So, a point here, quite far below the mean, has a low value on the vertical axis. That means that it's possible but unlikely to see this kind of a measurement. As we move closer to the mean value, the probability of seeing it or the likelihood of seeing it is higher and the mean is the most likely value here in the middle of the distribution. This distribution is symmetric, meaning that this side is a mirror image of this side. So likewise, as we move away from the mean and get higher and higher, we get values here that are certainly possible but they are less likely than the mean to occur. So, most variation is occurring in here in this space, close to the mean, meaning most of your data is here, but there is some small chance that if you collect enough data, you're gonna see some very extreme values that are quite different from the mean. This is what we're talking about when we say variation or variability in the data. We have a technical term for it, which is a variance. It describes how wide this distribution is and its square root is the standard deviation which we denote by sigma. We have a mean and a variance or standard deviation for this distribution. Suppose we had another distribution that had the same mean but it was narrower, meaning that seeing values farther away from the mean are even less likely. So, to see this same value here is even lower on the vertical axis and the chance of seeing the mean value here even though it's the same mean value, the chance is even higher. It's way up here on the vertical axis. So, this distribution being narrower is going to have a smaller variance or a smaller standard deviation. It has less noise than the blue distribution. That's what we mean when we talk about variation in your data.