Statistics is all about trying to make generalizations based on something we can actually see and measure - running an experiment, taking a survey, or considering evidence in a courtroom.

Any time we do this, there is a chance of drawing the wrong conclusion - what we commonly call false positives and false negatives.

In stats classes like K300 and S301, these errors are called by the more confusing names "Type I" and "Type II" errors. In this video Damon gives us an easy way to remember which is which, and why it's important for your exam!

How do I know when to use the t-test instead of the z-test?

Just about every statistics student I've ever tutored has asked me this question at some point. When I first started tutoring I'd explain that it depends on the problem, and start rambling on about the central limit theorem until their eyes glazed over. Then I realized, it's easier to understand if I just make a flowchart. So, here it is!

Basically, it depends on four things:

- Whether we are working with a mean (for example, "37 students") or a proportion (e.g., "15% of all students").
- Whether or not we know the
*population*standard deviation ($\sigma$). In real life we usually don't, but statistics courses like to contrive problems where we do. - Whether or not the population is normally distributed. This is mainly important when dealing with small sample sizes.
- The
*size*of our sample. The magic number is usually 30 - below that is considered a "small" sample, and 30 or above is consi...