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…)
One of the most difficult steps in calculating sample size estimates is determining the smallest scientifically meaningful effect size.
Here’s the logic:
The power of every significance test is based on four things: the alpha level, the size of the effect, the amount of variation in the data, and the sample size.
The effect size in question will be measured differently, depending on which statistical test you’re performing. It could be a mean difference, a difference in proportions, a correlation, regression slope, odds ratio, etc.
When you’re planning a study and estimating the sample size needed for (more…)