# How to compute the sample update error?

In the book Reinforcement Learning An Introduction，Chapter 8.5，there is an example that compares the efficiency of expected and sample updates:

According to the author, "In this case, sample updates reduce the error according to $$\sqrt{\frac{b-1}{bt}}$$ where $$t$$ is the number of sample updates that have been performed (assuming sample averages, i.e., $$\alpha = \frac{1}{t}$$." I just wonder how to compute the sample-update error(i.e.,How to get the formula $$\sqrt{\frac{b-1}{bt}}$$?