Presumably where you posted it, given that local feeds show posts based, not on if someone is on the instance, but rather which instance the post is made on. The model I used is litterally the most basic thing in the world, so I just cobbled something together that was somewhat meaningful. I only took college stats, so complex models are out of my range.

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.

Oh yeah ok, so I was going to figure out to put “H0 : L = 8.2”, and “H1 != 8.2, X~Po(8.2), P(c<=X<=c2) => c=?, c2=?” but I left it out because I couldn’t format it in a way that looked half decent in a Lemmy post.

I found the critical regions of the Poisson distribution, that takes the mean to be the average comments/post for the fediverse. I then interpreted those numbers, which I where I assume I’ve made a mistake. As if it was outside of the critical region, that would mean H1, but we know H1 is wrong, since we already have a value for L. It sounds like your interpretation of what I did is bang on. Yeah I get that it isn’t a hypothesis test, but at the level of my stats exams - finding the critical regions was 99% of the work in a hypothesis test.

I only took college level statistics like I said in another reply. I just thought it was cool to see all the instances comments/post ratio. It doesn’t help that my stats teacher was the most boring man alive, and I was always much preferred the pure side of the maths course.

Oh, you can absolutely increase how meaningful this is. I only took college level statistics so this is very rudimentary. But yeah, I don’t know how you would predict how long something would take to be seen etc.

I don’t have any data for upvote count/ comment based metrics. If you have any sites that happen to have that data, send it my way, that’d be amazing!

I’m not installing a fucking app to use my plane tickets. I hate flying enough already.

Which person? OP? The guy you are replying to? Or the spam voter?

Lemmy was for while like 50% bots, which is why you see random shifts generally.

Could be something else though