To me, it's obvious that limited data is better than no data, and you interpret it in context, evaluate theories for why you got that data (including stuff like sampling bias, etc.)
But I'm starting to suspect most people view data as being a binary support or not of a view?
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like, to me I can publish a little bit of data and I just default assume everyone knows that I'm not drawing hard conclusions from it, it's just a piece of evidence we can discuss, but people often respond like I think I'm making a bold, irrefutable claim about reality
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which... feels a little startling to me. Should I start placing "I'm not definitively assuming anything from this data, a little data is better than no data" disclaimer at the beginning of everything? I just had assumed sharing the sample size and source was serving that function
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Most people cannot draw reasoned conclusions from data.
They can only filter it against their existing biases and reject or accept
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I think many disagreements arise for any problem dimension under discussion when we do not consider the possibility of a spectrum existing and instead think in terms of binary or limited states.
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A lot of people assume that. But they’re wrong.
Your audience is probably better than average, and I don’t think you should add a disclaimer.
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People learn what an ideal sample is very early on, often one of the first things. The Platonic perfect sample you're rarely able to get.
Then they never unlearn it and never come to terms with the fact that in research the best sample is the one you've got.
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People will object to tentative evidence being offered for a position they disagree with or feel threatened by. It's an isolated demand for rigor.
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Limited data is only better than no data if you can interact with it correctly, otherwise it leads to misleading conclusions and unfounded confidence. Even if your audience is better at most than recognizing that, they probably still aren't good enough. Just food for thought
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