Comment on Determining the reason no one replied to your Lemmy post.

TropicalDingdong@lemmy.world ⁨5⁩ ⁨days⁩ ago

• Agosagror@lemmy.dbzer0.com ⁨5⁩ ⁨days⁩ ago

  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, or whatever. 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.

  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.

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  • TropicalDingdong@lemmy.world ⁨5⁩ ⁨days⁩ ago

    So lets just cover a few things…

    Hypothesis testing:

    The phrase “if your post got less than 4 comments, that was statistically significant” can be misleading if we don’t clearly define what is being tested. When you perform a hypothesis test, you need to start by stating:

    Null hypothesis (H₀): For example, “the average number of comments per post is λ = 8.2.”
    
    Alternative hypothesis (H₁): For example, “the average number of comments per post is different from 8.2” (or you could have a directional alternative if you have prior reasoning).
    

    Without a clearly defined H₀ and H₁, the statement about significance becomes ambiguous. The p-value (or “significance” level) tells you how unusual an observation is under the assumption that the null hypothesis is true. It doesn’t automatically imply that an external factor caused that observation. Plugging in numbers doesn’t supplant the interpretability issue.

    “Statistical significance”

    The interpretation that “there is a 95% probability that something else caused it not to get more comments” is a common misinterpretation of statistical significance. What the 5% significance level really means is that, under the null hypothesis, there is only a 5% chance of observing an outcome as extreme as (or more extreme than) the one you obtained. It is not a direct statement about the probability of an alternative cause. Saying “something else caused” can be confusing. It’s better to say, “if the observed comment count falls in the critical region, the observation would be very unlikely under the null hypothesis.”

    Critical regions

    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, 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. There are many, many reasons why a post might end up with a low count. Use the script I sent you previously and look at what happens after 5PM on a Friday in this place. A magnificent post at a wrong time versus a well timed adequate post? What is engagement actually telling us?

    Model Parameters and Hypothesis Testing

    It appears that you may have been focusing more on calculating the Poisson probabilities (i.e., the parameters of the Poisson distribution) rather than setting up and executing a complete hypothesis test. While the calculations help you understand the distribution, hypothesis testing requires you to formally test whether the data observed is consistent with the null hypothesis. Calculating “less than 4 comments” as a cutoff is a good start, but 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.

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    • Agosagror@lemmy.dbzer0.com ⁨5⁩ ⁨days⁩ ago

      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

      Truthly 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.

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      • TropicalDingdong@lemmy.world ⁨5⁩ ⁨days⁩ ago

        Well I appreciate the effort regardless. If you want any support in getting towards a more “proper” network analysis, I’ve dm’d you a link you can use to get started. If nothing else it might allow you to expand your scope or take your investigations into different directions. The script gets more into sentiment analysis for individual users, but since Lemmy lacks a basic API, the components could be retooled for anything.

        Also, you might consider that all a scientific paper is, at the end of the day, is a series of things like what you’ve started here, with perhaps a little more narrative glue, and the repetitive critique of a scientific inquiry. All scientific investigations start with exactly the kind of work you are presenting here. Then you PI comes in and says “No you’ve done this wrong and that wrong and cant say this or that. But this bit or that bit is interesting”, and you revise and repeat.

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