Hmm, I'm not so sure. Aren't there ways to do statistical analyses with smaller sample sizes? I feel like there must be some nuance here that the question is missing. Maybe we need to dig into the details of capability analysis a bit more.
I'm going to go with 'False' on this one. Sure, a larger sample is generally better, but I don't think you necessarily need a 'fairly large' sample to do a capable analysis with attribute data. It depends on the specific situation and the level of precision you're aiming for.
You know, I'm reminded of that old statistics joke - 'How do you make a small fortune in statistical consulting? Start with a large one!' But in all seriousness, I agree that a large sample is probably needed here to get meaningful results.
Hmm, I'm leaning towards 'True' on this one. Attribute data can be tricky to work with, and if you don't have a big enough sample, your results might not be reliable. Better to err on the side of caution and get a large sample set, in my opinion.
Well, I think the key here is the phrase 'statistically sound.' In my experience, you generally do need a fairly large sample size to draw valid statistical conclusions. But I'm not an expert, so I'd have to double-check the specifics for this kind of analysis.
I'm not too sure about this question. Conducting a capability analysis seems like a pretty advanced topic, and I'm not sure how large the sample set needs to be to be statistically sound. I'd have to do some more research on the methodology to feel confident in answering this.
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