I’m Agosagror. I do stuff.

  • 2 Posts
  • 28 Comments
Joined 6 months ago
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Cake day: January 3rd, 2025

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  • Trump is a dictator, at least he is certainly acting like one.

    All dictators, regardless of ideology, rule through fear. Being afraid is how they control people. People generally aren’t scared of the dictators themselves, I could defo beat trump 1 on 1, but rather are scared of what the mass of people that the dictator has scared into doing anything they want.

    I personally feel that being fearful of trump is playing into his power, you can be angery, and upset or anything else, but to be scared is to give him control of your mind, and your actions.



  • I don’t fully understand this, beyond the obvious wackyness. The notion that America could take Canada is slightly insane.

    I don’t doubt that they could take the cities in the south, but Trump doesn’t want those, he wants the mineral rich north, that’s covered in snow and ice.

    I suspect that Canadians would just end up fighting quite a gruelling guerilla war in the north, whilst allowing Americans to struggle. Until Trump calls it quits.







  • Look, I survived statistics class. I will stride to defend some of my post.

    but it doesn’t explain what alternative hypothesis you’re leaning toward—high engagement versus low engagement isn’t inherently “good” or “bad” without further context.

    Namely that much of the aim of it was to show that an metric like comment count doesn’t imply that it was a good or bad post - hence the bizarre engagement bait at the end. And also why all of the “good posts” were in quotes.

    you might add a step that actually calculates the p-value for an observed comment count. This would give you a clearer measure of how “unusual” your observation is under your model.

    I’m under the impression that whilst you can do a Hypothesis test by calculating the probability of the test statistic occurring, you can also do it by showing that the result is in the critical regions. Which can be useful if you want to know if a result is meaningful based on what the number is, rather than having to calculate probabilities. For a post of this nature, it makes no sense to find a p value for a specific post, since I want numbers of comments that anyone for any post can compare against. Calculating a p-value for an observed comment count makes no sense to me here, since it’s meaningless to basically everyone on this platform.

    Using critical regions based on the Poisson distribution can be useful to flag unusual observations. However, you need to be careful that the interpretation of those regions aligns with the hypothesis test framework. For instance, simply saying that fewer than 4 comments falls in the “critical region” implies that you reject the null when observing such counts

    Truthfully I wasn’t doing a hypothesis test - and I don’t say I am in the post - although your original reply confused me - so I thought I was, I was finding critical regions and interpreting them, however I’m also under the impression that you can do 2 tailed tests, although I did make a mistake by not splitting the significance level in half for each tail. :(. I should have been clearer that I wasn’t doing a hypothesis test, rather calculating critical regions.

    It doesn’t seem like you are saying I’m wrong, rather that my model sucks - which is true. And that my workings are weird - it’s a Lemmy post not a science paper. That said, I didn’t quite expect this post to do so well, so I’ve edited the middle section to be clearer as to what I was trying to do.