Thanks to those who participated in the DAYS demographics poll. The results are now on my blog

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    I didn’t know you’re a social statistician by trade. That sounds like a cool job! Also very challenging, if my current experience working through “Causal Inference in Statistics” by Pearl et al is any indication.

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    I love this so much! Thank you for teaching me about statistics and putting your skills to good use here!😁 I’m surprised that so many love it, given the number of beanies who seem to dislike it (on the wall).

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      I posted about the poll here, on db and on mdl. I wouldn’t be surprised if a large chunk of the ‘love’ vote came over from MDL. It would have been interesting to see this with a larger sample I think.

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        I was really impressed you had 95 respondents! But yes, larger sample would be even better – what would be a good size, would you say?

        I think what I enjoy most about data like this is that it leads to more questions…(although perhaps that’s also frustrating!). When shows garner such mixed responses, it really makes me wonder about those shows that are widely loved or widely panned, and what thread in these shows ties people from diverse backgrounds in agreement. The link to geography and/or culture is such an interesting one. Drawing conclusions about respondents from North America is probably very different from drawing conclusions about a country like Japan, due to their difference in homogeneity (this is a generalisation, or course).

        This reminded me of how much I enjoy your writing! I’ve read a few more of your blog posts, and found them so thought-provoking!

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          Oh thank you! I’m so glad you enjoy my writing. I wish I could do more of it but it’s something I have to fit in around everything else unfortunately.

          Re the survey: the size of the sample is less important than its representativeness. There’s no way to get a representative sample from this kind of survey so the larger the sample the better. Still, it’s more important to understand the ways in which your sample is biased and make sure that’s clear in your analysis.

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    Love the stats lesson – the number of times I have had to explain to people why their data is not as meaningful as they think it is 🤯

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      Oh yes it’s frustrating. And when I looked at the data I thought, “mostly this just tells me the demographics of who completed the survey, not the demographics of who loved or hated it”. But that would have made a boring post I think !

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