Normal Distribution Part II
October 15, 2006
Ok, so in the last post I described what a Normal Distribution was:
It’s a graph of the frequency of occurrence of values of a specific trait.
So here’s some more nuggets of information.
A Normal Distribution can be described using two values; the mean and the ‘standard deviation’ are all that’s needed to describe a normal distribution.
Here a picture showing what the mean and the standard deviation describe:
So the mean described the position of the normal distribution on the X-axis, while the standard deviation describes the width of the normal distribution.
Mathematicians and statisticians love the normal distribution because you can describe the whole thing with just two numbers.
So here are two normal distributions with the same mean, but different standard deviations:
.. and here are to normal distributions with the same standard deviation but different means:
It is important to understand that evolution can affect both of these values either independently, or at the same time.
The normal distribution is a probability function.
There is a set of statistical tests that are known as the Parametric tests. These are all based on the assumption that the data being tested come from a normally distributed set. This basically allows the tests to make mathematical shortcuts by using the mean and standard deviation to produce probability curves.
There is a probability formula for the normal distribution, but I’m not going to show it, because we don’t need to memorise it to understand statistics.
If you’re interested in writing software, check out my other blog: Coding at The Coal Face