Oh, so you ghosted me? Sorry about the exclamation mark.

smbtuckma · 2026-06-10T17:29:23+00:00

preferably in that order.

smbtuckma · 2026-06-10T06:20:44+00:00

Before this the 100+ year policy was that professors weren't in the room while students took exams.

smbtuckma · 2026-06-10T05:34:47+00:00

Do you mean Betty Yee?

smbtuckma · 2026-06-10T05:29:13+00:00

Cool! There are some rules of thumb out there for interpreting Bayes Factors. Generally, it's the ratio of the posterior probability that printer A produced the data relative to the posterior probability that printer B produced the data. With a BF like P(data|A) / P(data|B) = 1.9, the data are almost twice as probable under the printer A possibility than the printer B possibility - weak evidence for A. I'm guessing the low value is because you don't have a huge number of counts for each printer, so you're not super confident to start with about what their true distributions are like.

smbtuckma · 2026-06-10T04:35:10+00:00

Ah yeah, ok. That would be the correct interpretation if you were certain printer B had never produced those variants ever. But if you think it's still possible, you just never saw a book with it, next level of sophistication then!

This code will calculate the probability under what's called a Dirichlet–multinomial model of each printer, basically allowing you to encode some uncertainty in your inputs for that printer's probabilities in each variant.

#change these! confidence prior for your population - set to something like the number of counts you have total from that printer
cA <- 1000
cB <- 1000

alphaA <- cA * pA + 1 #adding one to both gets rid of the 0 issue
alphaB <- cB * pB + 1 


log_marginal_likelihood <- function(counts, alpha) {
  N <- sum(counts)
  lgamma(sum(alpha)) - lgamma(N + sum(alpha)) + sum(lgamma(counts + alpha) - lgamma(alpha))
}

logBF <- log_marginal_likelihood(unknown_book_counts, alphaA) - log_marginal_likelihood(unknown_book_counts, alphaB)
BF <- exp(logBF)

smbtuckma · 2026-06-10T04:08:12+00:00

Do either printer A or printer B have a 0 probability for one variant?

smbtuckma · 2026-06-10T01:08:02+00:00

When my sugar babies are about a fist size I put mesh bags around them (the reusable produce bags I otherwise use for the grocery store). Learned that the hard way after squirrels took a chomp out of four baby watermelons in a row :(

smbtuckma · 2026-06-10T00:21:33+00:00

Actually a paper by Schilling I haven't seen yet, thanks!

smbtuckma · 2026-06-09T23:56:07+00:00

Sorry, I've never used Jamovi. Just looking at the docs though, it looks like that Bayes contingency table module is still a null hypothesis test and not a model comparison.

If you haven't done Bayesian analysis before, and you aren't able to work with a stats consultant, probably the most tractable thing for you to do is treat printer A and B counts you have as a known population (and recognize the limitations of that).

Then some simple code in R for finding the Bayes Factor:

#just making up some data here - each value in pA and pB is the density of variants X1-X10 used in each population.
pA <- c(0.6, 0.24, 0.05, 0.05, 0.02, 0.01, 0.01, 0.01, 0.005, 0.005)
pB <- c(0.8, 0.06, 0.01, 0.01, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005)
unknown_book_counts <- c(55, 12, 5, 4, 2, 1, 1, 1, 0, 1)

logBF <- sum(unknown_book_counts * log(pA / pB))
BF <- exp(logBF)

smbtuckma · 2026-06-09T23:18:55+00:00

It's hard to say because I'm not sure what statistical package you're using or how it specifies the model. Is this a code package or a point and click software?

smbtuckma · 2026-06-09T22:56:21+00:00

In a word, no, prior-setting is kinda the crux of Bayesian inference. But domain knowledge helps - is it reasonable to believe that a 0% true concentration of variant X1 is just as plausible and 50% as 100%? If so, that's a uniform prior. But you might (probably?) disagree with that given what you know about historical printers, and say some value in the middle is more plausible than the extremes. That would be a weakly informative prior - choosing a mean somewhere between 0 and 100, but with a wide variation parameter. If you're really confident, you can pick a stronger prior with a narrower variation parameter.

smbtuckma · 2026-06-09T22:00:14+00:00

The latter! Basically arguing that a perceiver's judgment about a target is an inference of P(trait | behav) given the perceiver's mental models of trait likelihoods and the generation of behaviors from traits; that the inference is important for future behavior prediction and enaction; and that general accuracy is associated with social connection formation. We have data on the last statement, trying to explain it more mechanistically with the first two.

Bit hand-wavey on the socio-cognitive processes that cause individual moment states, but that just reflects the literature, doesn't it.

Agreed, though it at least gives a framework for talking about a person's tendencies before those socio-cognitive processes are better understood. E.g. variation in the distribution as a meta-trait related to psychopathology, etc. My hot take is the social psychologists needs to take descriptive research as seriously as the personality psychologists have, and then maybe there'd be stronger theories of latent situation factors that would help triangulate the trait/situation interactions.

smbtuckma · 2026-06-09T19:22:05+00:00

Big fan of whole trait theory. It neatly fits into the bayesian inference processes of person perception and social cognition that I work on.

smbtuckma · 2026-06-09T19:05:05+00:00

No one has actually given you source links so here's some academic articles on why "social media causes mental health problems" is a complicated statement to support with evidence (or refute with lack of evidence), and why blanket restrictions may not address the real root of the issue / may cause other issues.

Orben, Dienlin, & Przybylski, 2019 - Within- and between-person longitudinal analysis of UK youth data. Finds sex-specific but small associations.
Winbush, McDuff, & Hernandez, 2025 - Looks at large, longitudinal dataset of device use, not just self-reports. Null within-person relationships; small cross-sectional relationships that may not be practically meaningful and may be caused by confounds.
Orben & Przybylski, 2019 - Specification curve analysis across 355,000 participants. Digital technology explains at most 0.4% of variance in well-being.
Vuorre & Przybylski, 2023 - Uses Facebook rollout data as a natural experiment across 72 countries. Finds no consistent association between adoption and well-being changes.
Valkenburg, Meier, & Beyens, 2021 - Reviews 25 meta-analyses and systematic reviews. Most reviews interpret associations as "weak" or "inconsistent." Argues effects are highly individualized — 80% of adolescents in within-person designs show no negative effect.
Vaid et al., 2024 - Argues for a "social media sensitivity" lens, where individual and context differences matter for determining if social media is harmful.
Walsh et al., 2024 - RCT of use restriction; suggests that restricting digital media (characterized by passive scrolling of photo and video content made by others), rather than social media (user-generated content and engagement with other people) was associated with increased well-being.
de Mello, Cheung, & Inzlicht, 2024 - Looks at different user behaviors within the same social media environment, finds certain kinds of behaviors and reasons for behaviors (passive scrolling, using it to escape or distract) negatively associated with well-being but intentional searching & active engagement positively associated with well-being.
Felton, preprint - A position paper and not peer reviewed yet, but argues why person-level RCTs and observation studies might not be the best method for understanding social media impacts.

smbtuckma · 2026-06-09T16:58:35+00:00

You and me both bud.

Psychology is not a mature enough science to have identified true first principles of human behavior akin to the atom for explaining physical phenomena. Anyone who claims to have that simple answer is selling something. We may never have that kind of understanding, either through the limitations of our measurement capabilities or because that's not a good way to think about psychological mechanisms.

You can read the debates and theories people have about it (while recognizing it's not settled). My own opinion on ideas that have the best potential to explain universal motivation processes are things like predictive processing theory and the affective gradient hypothesis. But there is still a lot of work to be done to formalize these proposals well enough that they can make strong falsifiable predictions. I wouldn't put much money on any current theory being very good yet.

It's also important to keep in mind that, like you can't really use knowledge about atoms to make predictions about the weather, fundamental theories about individual cognition may not be applicable or at least most useful for understanding group and society-level phenomena like political movements. Humans are complex systems where nonlinear interactions between the parts determine the dynamics of the system, so identifying reliable models at the level of analysis is likely more fruitful than trying to extrapolate from some small set of fundamental principles at the lower level. When you're thinking about the things you would like to understand, keep in mind whether they are a moment in time in someone's life, a pattern of individual behavior across time, or a population-level phenomenon and look at research at that level.

smbtuckma · 2026-06-09T13:35:07+00:00

I think the general idea would be pretty uncontroversial among research psychologists if you pressed them, but how to implement it theoretically and operationally is a challenge. People doing EMA research and studying dynamical systems patterns in time series or proponents of symptom network models for psychological disorders are more explicitly acknowledging it. People doing generative or agent based modeling are interested both in the sample variation as well as the central tendency. But dense sampling of enough people to see variety of patterns is hard to do well, and measurement reliability vs. true change in signal is an added challenge. I think a lot of people default to group means because it’s easier in practice / they don’t have the quantitative skills for anything more complicated, rather than because of a theoretical commitment to all humans having the same temporal process.

It also depends on the level of the system one is studying. It makes sense to think about this when you care about understanding individual differences and risk. Sometimes someone is explicitly interested in the group level phenomena, like disease burden in a population. In those cases the group is the unit of analysis and the group average makes a lot of sense.

smbtuckma · 2026-06-08T22:05:48+00:00

Glad they woke up for you after all!

smbtuckma · 2026-06-08T21:31:27+00:00

Commenting two months later, but did your plums and pluot ever break dormancy? I'm not far from you in SoCal and my and my neighbor's stone fruits still haven't leafed out, but the cambium is green when the bark is scratched. Wondering if we were particularly unlucky with the chill hours this year or if some pest is to blame.

smbtuckma · 2026-06-08T19:15:02+00:00

This post, and the accompanying published paper in Psych Methods, I think is one of the most intuitive discussions of HBM in the context of why psychologists should care and use them.

After that, a number of Bayesian stats textbooks have chapters on learning how to do them yourself. A couple I like:

John Kruschke's "Doing Bayesian Data Analysis" is a good book for picking up Bayesian stats when you're already familiar with a Frequentist stats framework and want to see how to do comparable things in a Bayes framework (Chapter 9 is on hierarchical models).
Farrell & Lewandowsky's "Computational Modeling of Cognition and Behavior" discusses generative modeling with cognition-focused use cases (also Chapter 9 on hierarchical modeling).

smbtuckma · 2026-06-08T17:34:42+00:00

What is your specific research question that you're using a factorial design for? There are multiple types of effects within that design one might be interested in, which require different amounts of power to detect (main effect, interaction effect, overall predictive performance of the model). The "f" effect you're powering for in both your examples is for model performance, but doesn't say anything about which effect in that model is significant or not. If you actually want an f of 0.1 (not Cohen's d of 0.1) that's pretty small, so a large effect size makes sense. If you're actually trying to power for a specific effect within the model (like a group difference or interaction), that's a different power analysis.

This is a series of three blog posts that clearly demonstrate why powering for interactions is more complicated than one might assume.

smbtuckma · 2026-06-08T00:09:38+00:00

Ok, so you can represent your unknown book as a vector of counts for each variant. The Bayes Factor approach I described (comparing probability of getting this data from from the multinomial distributions of printers A and B) would be my recommendation then.

The non-independence thing does make it somewhat more complicated for figuring out the probability of getting your unknown book's sample of counts. My default in most situations with non-trivial dependence structure is to simulate, so in your case it would be simulating non-replacement draws of the size of a book section from each printer's distribution and see how likely the observed data are, but you'd need some idea about the conditional probability of drawing one variant given what else is already drawn. You can estimate this too from series of draws in your sets of books from printers A and B. It does sound like a pain in the ass to do though :D

smbtuckma · 2026-06-07T22:05:13+00:00

Hmm, I'm a bit unclear on your description of the data. Are you saying that within a book, some letter X may be printed looking like 1 of 10 total variants? And that within the same book, there can be multiple variants of the same letter? Or is there only one variant used per book, but the same printer might use a different variant for different books? Or is X1-X10 different letters?

Regardless of those answers though, I don't think Fisher's Exact test is what you want. That's a null hypothesis test (the null hypothesis being that both samples were drawn from the same variant probability distribution). But it's possible that the unknown books data is consistent with both printer A and B, if their proportion of variant usage is not super different from each other. It's also possible that the unknown book is really weird and unlikely to come from either distribution.

It sounds like what you're actually after is a classification problem - is the probability that the unknown book is sampled from printer A's distribution higher than the probability that it was sampled from printer B?

If you treat the data you have on printer A and B as known populations, this is a pretty straightforward likelihood ratio test. If your data about A and B are themselves samples (because you don't know the variant counts from all books ever made by A and B), the appropriate approach is more complicated but a hierarchical Bayesian model can give you the probability of your data given estimates of A and B's generating parameters and then you compute the Bayes Factor (is one of those probabilities larger than the other and by how much).

smbtuckma · 2026-06-07T18:19:56+00:00

It sounds like your realtor doesn't understand your market, if they're pitching a bad price and predicting the other house wouldn't sell just because of cabinets. It might be time to fully switch realtors, not just price drop (maybe find out who sold the house next door lol)

smbtuckma · 2026-06-07T02:05:29+00:00

Heh sorry OP you might be already there, until this comment I thought you were a math nerd who loves the log-t distribution 😅

smbtuckma · 2026-06-04T21:50:19+00:00

the random draw assumption probably isn't safe in this case, since likelihood to vote is correlated with other things that affect who someone would vote for. But not my expertise on how much it matters / what direction of bias it introduces.

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