A research study rarely involves just one single statistical test. And multiple testing can result in statistically significant findings just by chance.
After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times for each test. When you figure out the probability of Type I error across all the tests, that probability skyrockets.
There are a number of ways to control for this chance significance. And as with most things statistical, determining a viable adjustment to control for the chance significance depends on what you are doing. Some approaches are good. Some are not so good. And, sometimes an adjustment isn’t even necessary.
In this webinar, Elaine Eisenbeisz will provide an overview of multiple comparisons and why they can be a problem.
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One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable.
For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale.
Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the boundaries at 0 and 100.
Likewise, counts have a boundary at 0 and are discrete, not continuous. The general advice is to analyze these with some variety of a Poisson model.
Yet there is a very specific type of variable that can be considered either a count or a percentage, but has its own specific distribution.
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