Aneesh Sathe

By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.

I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress.

How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either.

Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both  null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.

It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent.

Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t.

The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs.

Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?

Richard McElreath says it best:

There is no method for making causal models other than science. There is no method to science other than honest anarchy.

Tonight we cast our nets  
In foreign waters  
Now we are new  
Tomorrow we’ll belong  
Then the sea'll tug and pull  
Time to be gone  
  
So, let’s  
Kiss the nymphs  
Meet the crabs  
In their bucket games  
Feel their stabs  
  
Our plans are small  
But they are spread  
A thousand ports  
Before we are dead

Image: Sailing off Gloucester (ca.1880) by Winslow Homer.

I don’t have any fun when I use LLMs to write. It may have perceived utility: popping out a LinkedIn article or two everyday. But I bet no one is actually reading. It’s a strip mall for a thumb stroll.

LLMs suck at writing. The summaries that LLMs give with the “Deep Research” are so poor in quality that I start to skim it. Yes, I skim the thing that is already a summary.

Let’s do a small experiment using the  Harry G. Frankfurt Essay On Bullshit:

Here are HGF’s opening lines:

“One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. Each of us contributes his share.”

Now here is ChatGPT o3, with the advantage of having HGF in the training data and extra contextualizing from me “theory of bullshit”:

Prompt: write me an introductory paragraph on the theory of bullshit

Result: “Bullshit isn’t just lying gone sloppy; it’s a distinct communicative strategy built on indifference to truth. Where the liar knows the facts and works to conceal or invert them, the bullshitter treats truth-value as irrelevant raw material—selecting, mixing, or inventing claims to achieve an effect (status, alignment, vibe) rather than to inform. The result is epistemic pollution: signals that…”

Look! It’s the secret flag of “content”:  “Blah isn’t just X; Blah is Y”.

I cannot assimilate this, it definitely can’t help me synthesize anything. There is always better human writing even if it is poorly written. It has a certain aliveness, and that aliveness is contagious enthusiasm. Vibe coding I can understand, you can see bad output in the final result. Vibe coding still manages to change something in my mind: knowing what I don’t want.

I don’t mind people using AI at all, I use it alllll the time. Writing with LLMs is no just fun. All this prompting and almost nothing changes in my mind. When an AI rearranges your thoughts it does not rearrange your brain.

In 1986, David S. Landes wrote the essay, ‘What Do Bosses Really Do?’. He argues that the historical role of the ‘boss’ was an essential function for organizing production and connecting producers to markets. Digital platforms have become the new bosses. Platforms have the same functions of market creation, labor specialization, and management, but they have replaced the physical factory floor with algorithmic management. While their methods are novel, platforms are the direct descendants of the merchant-entrepreneurs and factory owners Landes described, solving the same historical problems of production in remarkably similar ways.

So, why am I posting this on my own blog and not on a “platform”? I don’t view writing as a financial transaction. It is a hobby. By putting the financialization lens front and center, platforms are killing the mental space for hobbies. When you monetize tweets, you create incentive to craft tweets that create engagement in particular ways. Usually not healthy ways.

If we think of old media or traditional manufacturing, we can compare them to guilds. Guilds kept up prices and controlled production. With the simplification of tasks factories could hire workers who weren’t as highly skilled but didn’t need to be. Nowadays, why should any newspaper or TV channel’s output be limited by the amount of airtime or page space they have?

Platforms take unskilled and train them. We are in the age of specialization of ideas.  Akin to the “the advantage of disaggregating a productive process”  Platforms leverage this by having many producers explore the same space through millions of different angles. This allows the platform to “purchase exactly that precise quantity of which is necessary for each process” —paying a viral star a lot and a niche creator a little, perfectly matching reward to market impact. Which is to say platforms make money through whatever sticks.

In Landes’s essay, Management became specialized, today management will become algorithmized. Platforms abstract away the issues that factory owners had such as embezzlement of resources, slacking off etc. Platforms don’t care how much or how little you produce, or even if you produce. If you do, the cash is yours (after a cut of course).

This may lead to a visceral reaction against platforms. This week when Substack raised a substantial amount they called the writers “the heroes of culture”. This should ring at least a tiny alarm in your head. The platform’s rhetoric of the creator-as-hero is a shrewd economic arrangement. In the putting-out system, the merchant-manufacturer “was able to shift capital expenditures (plant and equipment) to the worker”. Platforms do the same with creative risk. The writer, artist, or creator invests all the time and labor—the “capital” of creation—upfront. If they fail, they bear the entire loss. The platform, like the putter-outer, only participates in the upside, taking its cut from the successful ‘heroes’ while remaining insulated from the failures of the many.

So what do platforms really do? They have resurrected the essential role of the boss for the digital age. They are the merchant-manufacturers who build the roads to market, and they are the factory owners who discipline production—not with overseers, but with incentive algorithms. By casting the creator as the hero, they obscure their own power and shift the immense risks of creative work onto the individual. While appearing to be mere background IT admins, they are, in fact, the central organizers of production, demonstrating that even in the 21st century, the fundamental challenges of coordinating labor and capital persist, and solving them remains, as it was in the 18th century, a very lucrative role.

What Do Bosses Really Do?, David S. Landes, The Journal of Economic History, Vol. 46, No. 3 (Sep., 1986), pp. 585-623 (39 pages). https://www.jstor.org/stable/2121476

A few days ago I wrote about the beauty of great documentation; this is the evil twin post.

The spectrum of meaning across the words hack, hacky, and hacker form a horseshoe when thinking about postures toward life. On either ends are the most difficult options. Being either a hack or a hacker requires dedication and both approaches narrow your world. Being hacky, taking imperfect shortcuts, in the world is immensely satisfying. It is play disguised as problem solving.

A successful hack takes tremendous effort and dedication just to pretend to be great at something. Humans are great at spotting and discarding hacks. It takes a true master to fool a large enough population and build financial columns under the smoke. Being a hack is constant desperation, there is no play. It is no way to live.

On the other end of the same horseshoe as the hack, is hacking. Here, you are actually achieving something difficult enough to require mastery. “Playfully doing something difficult, whether useful or not, that is hacking.” says Richard Stallman. Now, I’m all for the playful, the difficult, and the useful, but not the “or not”. At minimum hacking should be in service of a prank. Doing things just because is like felling a tree in a forest when no one is around. At least a jump scare is a sine qua non (the dictionary is working :P).

Most systems, especially computers are designed by people for people like you and me who are neither very bright nor very invested in the thing. We want to not have the problem. You can always walk away but that is neither fun, nor useful, and certainly not hard. My favored way is to take the Nakatomi Tunnel through problems. Be hacky. Try enough approaches, push buttons that may do the thing you want until the alignment is just so and you slip through. Effectiveness here = solving many real-world problems quickly while preserving playful momentum.

I want to draw a distinction here from the oversubscribed idea of jugaad. Jugaad was once framed as creative improvisation. It is not. I do not care for jugaad. To make something substandard and expect people to accept it is no way to be in the world. Build good stuff, be hacky route through the small issues.

A hacky mindset is a foxy mindset and not just in the Hendrix way. The Hedgehog and the Fox is a great essay by Isaiah Berlin where he talks about the two kinds of people in the world. Hedgehogs, are great at one big thing. Foxes are mediocre at many things. Foxes thrive on lateral moves and opportunistic shortcuts, you know, hackiness. The hacky, foxy approach to life is more my style.

Breadth, speed, and joy beat fakery and fixation every time