You can get this number yourself using the `qnorm` function.

The `qnorm` function is a little like the opposite of the `pnorm` function that we saw in the previous chapter. That function started with a `p` because it gave us a probability—the probability that we would see a value equal to or below it in a normal distribution. The `q` in `qnorm` stands for **quantile**. A quantile, for a given probability, is the value at which the probability will be equal to or below that probability.

I know that was confusing! stated differently, but equivalently, a quantile for a given probability is the value such that if we put it in the `pnorm` function, we get back that same probability:

> qnorm(.025)[1] -1.959964> pnorm(-1.959964)[1] 0.025 ...