The Normal Distribution
Ever wondered why so many things in nature follow a predictable pattern? The normal distribution is that pattern - a symmetrical, bell-shaped curve that describes how data clusters around an average value.
When we write X ~ N(μ, σ²), we're saying that variable X follows a normal distribution with mean μ and variance σ². The probability density function might look scary with all its symbols, but your calculator does the hard work - you just need to understand what it represents.
Here's what makes normal distributions special: they're perfectly symmetrical about the centre, where the mean, median, and mode are all identical. About 68% of values fall within one standard deviation of the mean, 95% within two standard deviations, and nearly all (99.8%) within three.
Key insight: The area under any part of the curve gives you the probability of values falling in that range - this is how you solve most normal distribution problems.
For a variable to follow a normal distribution, it must be continuous (can take any value), random (unpredictable for individual cases), and symmetrical about the mean. Think heights, test scores, or measurement errors - they all fit this pattern naturally.