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.
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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.
The Wording for Describing Confidence Intervals
Let’s look at an example. (more…)
Many of us love performing statistical analyses but hate writing them up in the Results section of the manuscript. We struggle with big-picture issues (What should I include? In what order?) as well as minutia (Do tables have to be double-spaced?). (more…)
One issue with using tests of significance is that black and white cut-off points such as 5 percent or 1 percent may be difficult to justify.
Significance tests on their own do not provide much light about the nature or magnitude of any effect to which they apply.
One way of shedding more light on those issues is to use confidence intervals. Confidence intervals can be used in univariate, bivariate and multivariate analyses and meta-analytic studies.
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