And use 1 ply with no bidet?

Savages.

Before enlightenment, caffeinate and defecate.

After enlightenment, caffeinate and defecate.

Drink enough coffee to shit before leaving the house.

cooked I tell ya

so…

an onion?

Wait. This isn’t the onion?

Yeah I wouldnt expect the elevation to be an issue? I’ve always found it growing at sea level/ warm climates. My understanding is that it can’t withstand cool temperatures. Its for a friends farm, but we don’t expect fruits for decades.

All basically sea level. I was in a longan & rambutan grove last week, with some trees as old as 80 years.

Nice! I’ve never seen anyone cut rambutans in half like that, but you do you. Are those longans I see?

The cut of the rambutans was a point of contention. I kept them as seed and in-spite of their perhaps, less than optimal cut, I did get plenty seedlings. And yes, longan and lilikoi and soursop.

On the calamondin, I think its at least partially due to it being a little more “wild” than the mexican lime and its planted near which got trimmed on the same day. The mexican lime “loves” being trimmed; it responds with almost extraordinary growth every time I trim it. The calamondin, well, its languished, and I literally planted them both on the same day, fertilize them same amount each time, trimmed them same day, everything same (even both 1 gallon plots when planted).

I think it might be more to do with how “bred for captivity” the mexican lime is compared with the more wild calamondin. Prior to the skeleton trim, the calamondin was far outperforming the mexican lime.

Bananas coming in. Harvested maybe… 40 lbs last week. Should have two more bunches about the same size over the next 1-3 weeks.

Soursop should be ready soon. Vanilla is still flowering. Cacao is coming in, but my main producing plant wasn’t planted in a good place so I had to dig it up and move it. It will take a few months/ years to get back to production, but thats a bummer because I was getting a couple pods a week. I did, however, take some pods and germinate them and have about 40 seedlings ready for planting, and another 40 on the way. I have a friend who just bought a farm that had been abandoned and they need plants.

Papaya (pawpaw) is coming in strong. Harvesting edamame soon. Calamondin, mexican lime, and bayers lime all coming in continuously now that we’re leaving the cool season, although I did a skeleton trim on my calamondin last year and it really hasn’t recovered like I had hoped. Its mexican neighbor responded far better to the pruning. I’m afraid I may have disabled it, because I was getting almost an entire fruit box of limes off that calamondin before I trimmed it, but it had a low fork and I was afraid it was going to rip its self apart as it got bigger. Eggplant, and asparagus also coming in stronger now that its warming up.

I traded some pizzas last weekend for about 3 five gallon buckets of mangos. About half were ripe so those all got cut up and frozen. Then the other half, the green ones, we cut up and pickled. But I don’t like li hing powder so we’ll do something else.

bananas from a few weeks ago:

breakfast:

oh yeah you get that with lemmy pro. if you upgrade to lemmy pro+ you get an animated banner for your profile too!

dude isn’t gender neutral?

Windows to my linux partition:

(this is why I wont dual boot)

I think of this meme daily.

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.

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.