Odds is confusing in a different way than some of the other terms in this series.
First, it’s a bit of an abstract concept, which I’ll explain below.
But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term.
I found this incorrect definition recently in a (non-statistics) book: (more…)
I’ve written about this before–there is just something about statistics that makes people feel…well, not so smart.
This makes people v-e-r-y reluctant to ask questions.
This fact really struck me years and years ago. Hit me hard.
It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.
It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.
Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.
What does it mean for two variables to be correlated?
Is that the same or different than if they’re associated or related?
This is the kind of question that can feel silly, but shouldn’t. It’s just a reflection of the confusing terminology used in statistics. In this case, the technical statistical term looks like, but is not exactly the same as, the way we mean it in everyday English. (more…)
Effect size statistics are required by most journals and committees these days — for good reason.
They communicate just how big the effects are in your statistical results — something p-values can’t do.
But they’re only useful if you can choose the most appropriate one and if you can interpret it.
This can be hard in even simple statistical tests. But once you get into complicated models, it’s a whole new story. (more…)
Any time you report estimates of parameters in a statistical analysis, it’s important to include their confidence intervals.
How confident are you that you can explain what they mean? Even those of us who have a solid understand of confidence intervals get tripped up by the wording.
Let’s look at an example. (more…)