I feel like I’ve seen practice questions where they emphasized that non-parametric tests don’t rely on normality, which makes me think C is definitely right.
This is a good opportunity to show my understanding of non-parametric methods. I know they're useful when the data doesn't fit the normal distribution assumptions, and for testing hypotheses about medians. I've got a solid strategy for this.
Wait, I'm not sure about the "no parameters" part. I thought non-parametric tests still had parameters, just different ones than parametric tests. I'll have to think this through step-by-step.
Okay, I've got this. Non-parametric tests are used when the data doesn't meet the assumptions for t-tests, like normality. And they focus on medians rather than means. I'm pretty confident I can nail this one.
Hmm, I'm a bit confused on the differences here. I know non-parametric tests don't require normal distributions, but I'm not sure about the other criteria. I'll have to review my notes carefully.
This looks like a tricky question, but I think I can figure it out. I'll need to remember the key differences between parametric and non-parametric tests.
I heard the exam committee was considering adding a 'choose all of the above' option, just to keep us on our toes. Non-parametric testing is like the party animal of statistical methods - it'll show up to the test, but it might not remember the answers.
Ah, the joys of non-parametric testing. It's like trying to hit a moving target with a blindfold on. But hey, at least we don't have to worry about those pesky normal distributions, right?
I'm pretty sure non-parametric testing is used when the data doesn't follow a normal distribution. Options C and D seem to be the correct answers here.
Barbra
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