# Statistics

Statistics is the study of analyzing data and making inferences about a population. That sounds intimidating, but here's the thing to remember—statistics is just another type of mathematics. And we know all about math. There are rules to math; if you use the right equation at the right time, then you'll get the right answer.

Here's a sneak peek at what we'll cover in this year:

We'll chat about all the different types of data out there, along with the best ways to display them. You don't graph categorical data with a histogram for the same reasons you don't eat Thanksgiving dinner with a spatula.

People won't stay awake to hear your conclusions if you insist on reading off all 500 data points you collected. We'll discuss how we can summarize our data using just a handful of numbers.

Sometimes our data are unruly, so we can use a transformation to get them to settle down.

Data don't appear out of thin air. Somebody has to go collect them. Once you've identified the population and parameters you're interested in, it's time to conduct a survey, experiment, or observational study to nab a sample.

Since statistics is about making inferences, we'll need some way to talk about how likely we think different events are. So, we'll wrap up the semester by boning up on probability. Plus, we'll run some simulations, which are a super-handy tool in our stats toolkit.

Statistics (like politics) is the art of the possible, so it's no surprise that probability plays a big role in it. We'll give you the ground rules that all random events obey.

Using our new probability knowledge, we'll dig into random variables and probability distributions. Like rolling dice or flipping coins, sampling data from a population involves a little uncertainty. At the same time, we don't expect to roll a 7 or land right on the edge; some outcomes are more likely than others.

The sampling distribution is the living, beating heart of statistical inference. Metaphorically speaking, of course. Which means it's not living, beating, or a heart. It is important for making any inferences from a sample, though.

At this point, we finally have the tools we need to make the leap from our incomplete sample to the total population. Want to know what the average length of a honey badger is? Just take a sample, measure the sample's average, and use the techniques of estimation to figure out a range of plausible values for the population average. Well, assuming you survive even a sample of honey badger attacks.

We'll also spend plenty of time talking about hypothesis tests. No, they're not the ones you were dreaming about. Instead, we use them to make a decision or come to a conclusion about the population. They're a great way to distill our data down to a simple yea-or-nay vote.

Of course, it would be hard to learn statistics' rules without a caravan of readings, guided questions, problem sets, and activities, so we've got all that covered, too.