This site is built around the idea that the most important use of statistics is to support scientific reasoning. If this is the goal then statistics should not be taught simply as a menu of procedures to use ritualistically. Statistical procedures, or the software in which they are implemented, are not able to do the thinking required of good science.
Similarly, there are different ways to do statistics. Some ways can get you results quickly, but you might not really understand what you did. Other ways may require more time and effort, but also promote a better understanding. The biggest barrier and least appealing aspect of statistics for most people is the math. Statistics is math, so there is really no way to avoid it. You have to know the math to understand statistics. But let’s be clear on what we mean by math. Math is more than computational procedures. Fundamentally, math is formal reasoning, and it is this aspect of math that is most important to understand if you want to master statistics. We can get a computer to do the computational aspects of math for us, but, at least for now, computers will not replace good reasoning. But, a good statistical programming language can allow us to pass off the tedious computations to the computer while still being very close to the reasoning needed to understand statistical methods.
This site demonstrates quantitative methods commonly used in the social sciences, using statistical programming. There are two primary goals for developing these demonstrations.
Speaking of formal statistical training, this site is aimed at supplementing – but not replacing – the more traditional modes of learning statistics. Statistical concepts can be complex and are not always intuitive. So, mastering the basics of statistics requires active practice using the concepts and procedures taught in the classroom. Indeed, one reason for creating this site it to use as a resource supplementing my own teaching of statistics. So, while lectures are helpful, they are just the beginning. We have to apply what we learn to become proficient with statistical methods. With statistics, like many things in life, we learn by doing.
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This is a list of courses I teach( or plan to teach) at Auburn University and that are relevant for this site: