In This Chapter
Beware of significance
Be wary of graphs
Be cautious with regression
Be careful with concepts
The world of statistics is full of pitfalls, but it's also full of opportunities. Whether you're a user of statistics or someone who has to interpret them, it's possible to fall into the pitfalls. It's also possible to walk around them. Here are ten tips and traps from the areas of hypothesis testing, regression, correlation, and graphs.
As I say earlier in the book, "significance" is, in many ways, a poorly chosen term. When a statistical test yields a significant result, and the decision is to reject H0, that doesn't guarantee that the study behind the data is an important one. Statistics can only help decision making about numbers and inferences about the processes that produced them. They can't make those processes important or earth shattering. Importance is something you have to judge for yourself — and no statistical test can do that for you.
Let me tell you a story: Some years ago, an industrial firm was trying to show it was finally in compliance with environmental cleanup laws. They took numerous measurements of the pollution in the body of water surrounding their factory, compared the measurements with a null hypothesis-generated set of expectations, and found that they couldn't reject H0 with α = .05. Their ...