## Defining parametric tests in statistics

We’ve been throwing around the term a lot in this series. I’ve been saying in parametric statistics this, in parametric statistics that, but I kept putting off giving a definition. It’s not because it’s hard to understand, it’s just that typically when you’re doing statistics you already know if you’re using a parametric test, but because we try to make no assumptions in this series, we’re going to put this to bed once and for all. Today we’re talking about parametric statistics!

(more…)## Independence in statistics

A while back we introduced the central limit theorem, it was a way to take data and make it normal (gaussian) as if by magic, which is one of the assumptions needed for parametric statistics (the most commonly used kind). Today we’re introducing another assumption, that the data are independent. The idea of independent events is probably straightforward, but it’s yet another bedrock of statistics that we should talk about in depth to help us understand why things are the way they are.

(more…)## Day 22: Parametric vs. NonParametric Statistics

Technically we *could *call this parametric statistics part 2. However, since we are covering nonparametric statistics and more importantly the difference between parametric and nonparametric statistics, it would seem that this title makes more sense. As usual with a continuation, you probably want to start at the beginning where we define parametric statistics. Ready to get started?*

## Day 21: Defining Parametric Statistics

Well my lovely readers, we’ve made it to the three week mark, 5.7% of the way through! Okay maybe that doesn’t seem like a big deal written like that, but hey it’s progress. So last post we had our independence day, or rather defined what it meant to have independent events vs. dependent events. We also said it was an important assumption in parametric statistics that our events are independent, but then we realized we never defined what parametric statistics even is, oops. So let’s stop dragging our feet and talk parametric statistics!*