By far the most useful of all continuous distributions is, by a stroke of real bad luck, one of the harder ones to handle mathematically. It's called the normal distribution, and for technical reasons it models extremely well a large variety of phenomena in the real world, such as heights, weights, IQ, exam scores, etc.
A normal distribution is specified by two things: the mean, \mu , which is an average value, and the standard deviation, \sigma , which is a measure of how spread out the distribution is. A random variable that is, as we say, distributed N(\mu ,\sigma ) has PDF
f\left(x\right)=\frac{1}{\sqrt{2\,\pi }\,\sigma }e^{-\left(x-\mu \right)^{2}/\left(2\sigma ^{2}\right)}.
Figure 1 illustrates this PDF for \mu =100, \sigma =15.
Figure 1: Plot of the PDF for N(100,15) showing P(X<120).
The difficulty arises because we have no way to integrate this function, meaning there's no way to express its CDF in a nice way in terms of known functions. That's bad news, because it's the CDF that we use to calculate actual probabilities (the PDF is more of theoretical use).
The way we get round this is very clever. It turns out that if X\sim N(\mu ,\sigma ), then if we calculate
Z=\frac{X-\mu }{\sigma },
we find that Z\sim N(0,1). We call Z the standard normal variate. Because any normal random variable can be converted into Z, it follows that we only need to know the CDF of Z.
This CDF can be calculated numerically and tabulated.
\begin{array}{cccccccccccc} \text{z} & \text{} & 0. & 0.01 & 0.02 & 0.03 & 0.04 & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 \\ \text{} & \text{} & \text{} & \text{} & \text{} & \text{} & \text{} & \text{} & \text{} & \text{} & \text{} & \text{} \\ 0. & \text{} & 0.5000 & 0.5040 & 0.5080 & 0.5120 & 0.5160 & 0.5199 & 0.5239 & 0.5279 & 0.5319 & 0.5359 \\ 0.1 & \text{} & 0.5398 & 0.5438 & 0.5478 & 0.5517 & 0.5557 & 0.5596 & 0.5636 & 0.5675 & 0.5714 & 0.5753 \\ 0.2 & \text{} & 0.5793 & 0.5832 & 0.5871 & 0.5910 & 0.5948 & 0.5987 & 0.6026 & 0.6064 & 0.6103 & 0.6141 \\ 0.3 & \text{} & 0.6179 & 0.6217 & 0.6255 & 0.6293 & 0.6331 & 0.6368 & 0.6406 & 0.6443 & 0.6480 & 0.6517 \\ 0.4 & \text{} & 0.6554 & 0.6591 & 0.6628 & 0.6664 & 0.6700 & 0.6736 & 0.6772 & 0.6808 & 0.6844 & 0.6879 \\ 0.5 & \text{} & 0.6915 & 0.6950 & 0.6985 & 0.7019 & 0.7054 & 0.7088 & 0.7123 & 0.7157 & 0.7190 & 0.7224 \\ 0.6 & \text{} & 0.7257 & 0.7291 & 0.7324 & 0.7357 & 0.7389 & 0.7422 & 0.7454 & 0.7486 & 0.7517 & 0.7549 \\ 0.7 & \text{} & 0.7580 & 0.7611 & 0.7642 & 0.7673 & 0.7704 & 0.7734 & 0.7764 & 0.7794 & 0.7823 & 0.7852 \\ 0.8 & \text{} & 0.7881 & 0.7910 & 0.7939 & 0.7967 & 0.7995 & 0.8023 & 0.8051 & 0.8078 & 0.8106 & 0.8133 \\ 0.9 & \text{} & 0.8159 & 0.8186 & 0.8212 & 0.8238 & 0.8264 & 0.8289 & 0.8315 & 0.8340 & 0.8365 & 0.8389 \\ 1. & \text{} & 0.8413 & 0.8438 & 0.8461 & 0.8485 & 0.8508 & 0.8531 & 0.8554 & 0.8577 & 0.8599 & 0.8621 \\ 1.1 & \text{} & 0.8643 & 0.8665 & 0.8686 & 0.8708 & 0.8729 & 0.8749 & 0.8770 & 0.8790 & 0.8810 & 0.8830 \\ 1.2 & \text{} & 0.8849 & 0.8869 & 0.8888 & 0.8907 & 0.8925 & 0.8944 & 0.8962 & 0.8980 & 0.8997 & 0.9015 \\ 1.3 & \text{} & 0.9032 & 0.9049 & 0.9066 & 0.9082 & 0.9099 & 0.9115 & 0.9131 & 0.9147 & 0.9162 & 0.9177 \\ 1.4 & \text{} & 0.9192 & 0.9207 & 0.9222 & 0.9236 & 0.9251 & 0.9265 & 0.9279 & 0.9292 & 0.9306 & 0.9319 \\ 1.5 & \text{} & 0.9332 & 0.9345 & 0.9357 & 0.9370 & 0.9382 & 0.9394 & 0.9406 & 0.9418 & 0.9429 & 0.9441 \\ 1.6 & \text{} & 0.9452 & 0.9463 & 0.9474 & 0.9484 & 0.9495 & 0.9505 & 0.9515 & 0.9525 & 0.9535 & 0.9545 \\ 1.7 & \text{} & 0.9554 & 0.9564 & 0.9573 & 0.9582 & 0.9591 & 0.9599 & 0.9608 & 0.9616 & 0.9625 & 0.9633 \\ 1.8 & \text{} & 0.9641 & 0.9649 & 0.9656 & 0.9664 & 0.9671 & 0.9678 & 0.9686 & 0.9693 & 0.9699 & 0.9706 \\ 1.9 & \text{} & 0.9713 & 0.9719 & 0.9726 & 0.9732 & 0.9738 & 0.9744 & 0.9750 & 0.9756 & 0.9761 & 0.9767 \\ 2. & \text{} & 0.9772 & 0.9778 & 0.9783 & 0.9788 & 0.9793 & 0.9798 & 0.9803 & 0.9808 & 0.9812 & 0.9817 \\ 2.1 & \text{} & 0.9821 & 0.9826 & 0.9830 & 0.9834 & 0.9838 & 0.9842 & 0.9846 & 0.9850 & 0.9854 & 0.9857 \\ 2.2 & \text{} & 0.9861 & 0.9864 & 0.9868 & 0.9871 & 0.9875 & 0.9878 & 0.9881 & 0.9884 & 0.9887 & 0.9890 \\ 2.3 & \text{} & 0.9893 & 0.9896 & 0.9898 & 0.9901 & 0.9904 & 0.9906 & 0.9909 & 0.9911 & 0.9913 & 0.9916 \\ 2.4 & \text{} & 0.9918 & 0.9920 & 0.9922 & 0.9925 & 0.9927 & 0.9929 & 0.9931 & 0.9932 & 0.9934 & 0.9936 \\ 2.5 & \text{} & 0.9938 & 0.9940 & 0.9941 & 0.9943 & 0.9945 & 0.9946 & 0.9948 & 0.9949 & 0.9951 & 0.9952 \\ 2.6 & \text{} & 0.9953 & 0.9955 & 0.9956 & 0.9957 & 0.9959 & 0.9960 & 0.9961 & 0.9962 & 0.9963 & 0.9964 \\ 2.7 & \text{} & 0.9965 & 0.9966 & 0.9967 & 0.9968 & 0.9969 & 0.9970 & 0.9971 & 0.9972 & 0.9973 & 0.9974 \\ 2.8 & \text{} & 0.9974 & 0.9975 & 0.9976 & 0.9977 & 0.9977 & 0.9978 & 0.9979 & 0.9979 & 0.9980 & 0.9981 \\ 2.9 & \text{} & 0.9981 & 0.9982 & 0.9982 & 0.9983 & 0.9984 & 0.9984 & 0.9985 & 0.9985 & 0.9986 & 0.9986 \\ 3. & \text{} & 0.9987 & 0.9987 & 0.9987 & 0.9988 & 0.9988 & 0.9989 & 0.9989 & 0.9989 & 0.9990 & 0.9990\end{array}
Here's how it might work in practice. Suppose an IQ score X is normally distributed with mean 100 and standard deviation 15, and suppose we want to find the probability that X<120. We start by observing that
P\left(X<120\right)=P\left(\frac{X-100}{15}<\frac{120-100}{15}\right).
But this is simply
P\left(Z<1.3333\right).
We have from the table that
P\left(Z<1.33\right)=0.9082,
and that
P\left(Z<1.34\right)=0.9099.
Interpolating, we estimate that
P\left(Z<1.3333\right)=0.9082+0.33\times \left(0.9099-0.9082\right)=0.9087.
