2 posts tagged with "Distributions"

In my last two posts, I introduced measures of central tendency and the normal distribution.  Why?  Mostly because they are important statistical concepts but also because they lead nicely into the topic of statistical testing. 

Statistical testing is very important because it provides a level of confidence providing that results from experiments are not due to chance or luck (good or bad). There are at least 4 key steps to perform in statistical testing.  I say at least because for some of these you could easily break them down into smaller steps.  Oftentimes, these are implied or done so seamlessly that it doesn’t seem like they are steps at all but trust me, they are.  Each step will be discussed in turn but they are:

1. Create a hypothesis
2. Select the appropriate statistical test
3. Determine the test statistic for your hypothesis
4. Determine if the hypothesis should be rejected (statistically significant) or not rejected (statistically insignificant)

Just a note here, I will not be going into detail behind the math of these.  My goal is to explain the concepts behind statistical testing and provide two examples of their use.

As I mentioned in my previous post on measures of central tendency, this is part 2 of a 3-part post on some basic statistical concepts.

If you’ve ever been graded on a curve or have heard the term “bell curve”, then you know a bit about the normal distribution.  This post will focus on the concept of the normal distribution and some very basic elements around it.  Although this concept might seem fairly straightforward, there is a lot of detail and math that goes into it, which I will not be going into here.  Also, the normal distribution is only one of MANY distributions, which I also won’t cover.