1. mixingmemory
  2. The Basics of Statistics

The Basics of Statistics

By mixingmemory on June 27, 2007.

So the other day, I was talking to someone about one of the studies I was planning on posting about. I mentioned one of the results, and he said he'd really like to see the means and standard deviations. I thought to myself, "Alright, I'll put those in the post," but when I actually started writing the post, I began to fear that including standard deviations might just be confusing to people who don't have any background in statistics. So I left them out. But I didn't feel very good about leaving them out. So I decided to take action, and write a series of posts on the basics of the sorts of statistics that are used in the research I and other behavioral science bloggers post about.

I've now written 8, yes 8 of them. The topics I have so far are:

  1. Normal Distributions
  2. Standardized Normal Distributions and Z-Scores
  3. Samples, Sampling Distributions, and t-Distributions
  4. Confidence Intervals
  5. Hypothesis Testing With Confidence Intervals
  6. T-Tests

Plus there are two posts that are just examples that you can work through. Anyone familiar with statistics will notice that I've left out a whole bunch of stuff, and if you'll read the posts, you'll notice that I've left out or glossed over even more, but my purpose isn't to provide a complete blog course on statistics in the behavioral sciences. I just want to give people the basics, so that they can read posts and perhaps even the results sections of some of the papers cited in posts, and have a good idea of what's going on. I've written them all over the last 2 days because I've been sick, which means I've been bored. Since I've written them quickly, I've probably made some mistakes. If you notice any, point them out to me. I'll put up the first post Friday, and the rest every 2 or 3 days until we get through them all.

I'm not really sure how far I should go. After t-tests, I'll probably do correlations and then significance tests related to correlations. The next natural step after that would be ANOVA, and I'm not really sure how to explain ANOVA in a blog post except at a really superficial level. If I figure it out, I'll do ANOVA. I'll probably end with non-parametric tests. If you think I'm missing something vital in this list, let me know that too.

Tags
Blogs and Blogging

More like this

The Basics of Statistics IV: Confidence Intervals
So far we've been talking about different distributions and their parameters. If we're looking at a population with known parameters, then we're going to be dealing with either a normal distribution or a standardized normal distribution (Post I and II). If we're dealing with samples, we're going to…
The Basics of Statistics II: Standardized Normal Distribution and Z-Scores
So in the last post, we talked about the normal distribution, and at the very end, discussed that if you knew the mean and standard deviation of a population for a particular variable, than you can compute the probabilities associated with a particular value of that variable within that population…
The Basics of Statistics I: The Normal Distribution
So here's the first post on statistics. If you know the basics, and I suspect most of you do, then you can just ignore these posts (unless you want to check to make sure I'm getting it right). If you don't know the basics, then hopefully you will when I'm done. Even for those of you who've never…
Some Thoughts About the Statistics of the Human Microbiome
Reporting on the human microbiome--the microorganisms that live on and in us--is quite the rage these days. As someone who is involved in NIH's Human Microbiome Project, it's a pretty exciting time because the size and scale of the data we're able to generate is unprecedented. This also means we…

The only baseball statistic I care about is the number of games between the Braves and first place in the NL East... heh.

I wonder if something about power, effect size and degrees of freedom would be useful

Austin, I talk about df, of course, 'cause you need to to understand t-distributions and anything that uses them. But I didn't talk about power and effect size. Maybe I'll do that in a separate post, getting into the details of hypothesis testing with statistics a little more (Type I and Type II errors, for example).