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…)
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.
Oops—you ran the analysis you planned to run on your data, carefully chosen to answer your research question, but your residuals aren’t normally distributed.
Maybe you’ve tried transforming the outcome variable, or playing around with the independent variables, but still no dice. That’s ok, because you can always turn to a non-parametric analysis, right?
Well, sometimes.
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You probably learned about the four levels of measurement in your very first statistics class: nominal, ordinal, interval, and ratio.
Knowing the level of measurement of a variable is crucial when working out how to analyze the variable. Failing to correctly match the statistical method to a variable’s level of measurement leads either to nonsense or to misleading results.
Ratios are everywhere in statistics—coefficient of variation, hazard ratio, odds ratio, the list goes on. You see them reported in the literature and in your output.
You comment on them in your reports. You even (kinda) understand them. Or, maybe, not quite?
Please join Elaine Eisenbeisz as she presents an overview of the how and why of various ratios we use often in statistical practice.