Hi everyone, I have a cyclic/Latin-square-style counterbalanced repeated-measures design (as shown in image above), and I’m trying to work out the right way to use a Generalized Linear Mixed Effects model to analyse the data for it as I’m new to GLMMs. My aim is to compare how different cell-culture compositions affect the firing rates (expected to be of lognormal or tweedie distribution) of neural cultures while they’re being “trained” to perform tasks.

The study uses 100 neural cultures split evenly into two experiments of 50. Experiment 1 uses Culture Type A and Experiment 2 uses Culture Type B (so culture type is nested within experiment, not crossed). Each experiment’s 50 cultures are prepared in two independent batches of 25, and within every batch the cultures are made up with one of three cell-culture compositions (A, B, or C) - giving four batches and all three compositions in both experiments.

Each culture is then trained under 9 environmental conditions in a counterbalanced order, performing 2 tasks per environmental condition at 600 trials per task.

The GLMM model I currently planning to use is:

Firing rate ~ Culture Type + Batch + Cell Culture Composition + Environmental Condition + Task + (1 | Culture ID)

However, I feel that culture ID is insufficient as a random effect and it should include the other variables too, although I am unsure of what combination to use, especially since there’s so many of them. I am also worried about having to add dispersion models in the case of over/under-dispersion as well, since my response variable is not normally distributed. Hence, I would like to seek comments and advice on my current model from those who are experienced in working with GLMMs. Thank you very much in advance for the help.

Hi everyone,

I recently started a master’s degree in Data Science at a Mathematics Department. I’m still unsure which area to specialize in: statistics and data science touch so many fields, which is both the best and the hardest part of the journey. I’m particularly drawn to socio-economic and political topics, and journalism fascinates me.

I’m also struggling with how fast this field moves. Even when I try to stay as informed as possible, I constantly feel one step behind, even with respect to my own coursework. How do you, as students or professionals, keep up in such a fast-changing field, and above all, how do you find your bearings without feeling overwhelmed?

If you have the time, I’d love some advice on both of these questions, and on how to gain hands-on experience in the areas I’m interested in, for example through projects, internships, or communities/organizations where these “passions” and skills come together.

Especially interested in hearing from anyone with a background in statistics, whether you studied it, work in it, or moved into data science from it. How did your statistical foundations shape your path, and what would you do differently?

Thanks in advance for any suggestions, and I’m happy to answer questions about my program or interests!

I am examining interannual variations in annual precipitation and have noticed a sustained increase following a particular year. However, my dataset covers only nine years (9 points). What statistical approaches would be appropriate for testing whether this apparent increase is statistically significant and unlikely to have occurred by chance, given the short time series?

Hi, I'm planning a job switch and targeting quant fin/eq research kind of roles for which I need to make sure I'm good at these topics. Though I have studied both back in college days, but once again I'm planning to learn them from scratch and go to an advance level. Is there any single resource that takes me from very basic to pro level? If not, could anyone suggest the path that i should follow? Thanks!

Hi everyone! Im trying to model species richness as a function of mowing and soil nitrogen. My experimental design includes 20 strips of grassland, 10 mowed and 10 unmowed. They alternate such that mowed are always next to unmowed and form 10 blocks to account for spatial effects (block 1 = plots 1 and 2, block 2 = plots 3 and 4 and so on). Within each plot, we applied 3 levels of N, so subplots A, B, and C exist in each plot. I’m using a linear mixed effect model but I’m unsure whether the structure should be: (richness ~ mowing x soilN + (1|block)) or whether it should be (richness ~ mowing x soilN + (plot/block)). I’m going back and forth bc the N treatments within a plot are not independent but does having a random effect of plot mask the effects of the mowing treatment? Thank you!

i am graduated from engineering college just need some revision and brush which covers everything all concepts and terms .
like basic mean mode median
variance, correlation

and more moderate level terms as well to have good grip of stats

Coincidences stress me out, and I can't help getting superstitious about them sometimes, it's not even spiritual, it's psychological. But sometimes, after googling the things that happen to me, I find out that never going through that coincidence would be rarer than going through it. Would there be less superstituous people if they were more informed about statistics and data?

Help with statistical tests, t tests in particular!

I haven't taken a statistics class in about 18 years, but a recent side project dragged me back in. I built a deterministic, text-based dungeon crawler and ran some automated agents (seven different LLMs) through it to test how they navigate under incomplete information. Now I'm trying to figure out which performance gaps are statistically significant, and I'd love a sanity check on my math.

What I did:

• Calculated 95% Wilson intervals for escape rates (N=100 runs gave me a margin of ~±10 points; N=20 was ~±20).

• To benchmark them against a human, I played 20 specific labyrinths, and each agent played those exact same 20. I ran an exact two-sided McNemar test on the discordant pairs (the runs where one of us escaped and the other failed).

• That created 16 comparisons (7 models in 2 configurations + a Random bot + a heuristic bot) , so I applied Holm's correction. Six remained significant; for the other ten, I concluded it as too few labyrinths to tell.

• For model against model I only compared the Wilson intervals, even though all of them played the same 100 labyrinths.

• To test if labyrinth difficulty predicts who escapes, I calculated a concordance score using 2,000 bootstrap redraws for the confidence interval.

Where I’m second-guessing myself:

1. Is McNemar + Holm too conservative for only 20 paired labyrinths? Is there a more powerful test I should have used for this sample size?

2. I capped my batches at 100 runs per model configuration (so 200 per model) purely based on my API budget, not a proper power calculation. How would you have formally sized this?

3. Since all the agents played the exact same 100 labyrinth seeds (each was played labyrinth once, and the agents are stochastic), should I be evaluating this with a mixed-effects model (using the specific labyrinth as a random effect) rather than running individual pairwise tests?

4. Should I be running paired tests between models too, and how would that change what 100 runs can separate?

5. Are there any glaring red flags here that would fail peer review?

I dusted off R for the first time in almost 2 decades to run these initial calculations, and I was painfully slow. If there is a more elegant way to approach this, I'm all ears. (I know I could have handed this to the "all powerfull" LLM chat window, but then, there is no fun in discovery left)

(For full context,

• the plain-English write-up of my methods is here: https://signalanddrift.dev/notes/what-the-dark-tells-statistics/

• the project context is here: https://signalanddrift.dev/essays/what-the-dark-tells/)

Need help, please!

Trying to run a stat analysis for a project at work. I conducted a pre- and post survey (nominal > ordinal i believe?), and comparing it to ordinal data (5 point Likert scale and another ordinal scale). I was going to run a chi square test to determine whether the pattern of responses differs significantly between pre- and post-test groups. I was wondering if I should run Mann-Whitney U to test whether the ordinal responses differ between pre- and post-test, or if i should run kendall's tau-b, spearmans rho, or another statistical test? Thank you guys in advance!

I'm comparing two sources that each give a daily probability that an asset closes higher than today. one is my own simple model, the other is a published forecast. about 90 days of data for each.

so far brier score is almost the same for both (0.243 vs 0.239). log score shows a bigger gap, mostly because my model sometimes says 85% and gets it wrong.

questions:

1. which score makes more sense here, or should i report both?

2. is 90 days enough to say one is better, or is the difference just noise?

3. how do i test that properly, a paired test on the daily score differences?

not homework, just a side project. thanks

So, recently I've just finished stats 2, and other than spamming past papers I have nothing else to do. I'm wondering if there are any topics that I should learn to have some advantage in uni. Fyi im currently a senior

I am required to use Jamovi to analyze data from a survey I sent out but I am trying to test one variable as a moderator to the strength of the relationship between two other variables. Does anyone have experience with this?

And you are resource-constrained and unable to get more samples? Let’s say sample size is n = 3 to 7

What real life examples have such a scenario?

(I am not a conspiracist. I have an interest in statistics. This led me to the following chain of thoughts):

There is an astonishing number of stories about half-documented but supposedly world-changing inventions, such as hyperefficient cars or generators — whose inventors died suddenly and mysteriously right between completion and patenting of the device.

Are those too many deaths for them to be coincidences?

To be clear: A viable non-conspiratory explanation would be that we simply never hear about all those inventors who did NOT die: because they and their inventions get documented and analysed so extensively that they are exposed as untenable. Hence no urban legends, mysteries and stories arise.

It is also clear that the the mystery itself has an allure: everyone loves a good story, a component of mystery, and of course the idea of paying less for energy.

So there is no question where all the conspiracy theories come from.

But something is NOT explained: All the deaths.

Statistically, it is of course to be expected that some inventors die precisely in the phase between completion and patenting (or sale) of their invention - regardless of cause of death.

But HOW MANY inventors would it take to explain all the documented cases (i.e. minus the urban legends) of inventors of allegedly revolutionary devices dying in precisely this phase? Let me underline how specific this question is: it is not about how many inventors (including hobbyists) happen to be active at any given moment. Nor about how many are researching in this precise field. It is about how many are active in this precise field and are at exactly the point in their research where they are sitting on a finished, viable, functioning device that is only waiting for the patent to be granted!

How many might that be?! I'd value your guesses.

Meanwhile, let's look at sudden mortality, i.e. not from prior conditions. I asked AI to aggregate the numbers: "the probability of an average person in the United States suddenly dying on any given day is roughly 1 in 260,000 for a 30-year-old male, and 1 in 46,000 for a 55-year-old male"; another AI comes to slightly different numbers but very much in the same range.

And how long does a patent procedure take? Again, I asked AI to aggregate the numbers: "Utility patents take 24-36 months from filing to grant. Design patents are faster, usually taking 15-21 months." Again, another AI confirms.

Let's take the worst case of 3 years. How great is the probability of dying within these 3 years? It is above figure of 40,000 to 260,000 divided by the 1095 days in 3 years. (I asked AI as well, and it confirms: it's 1:250 for a 30-year-old, and 1:40 for a 55-year-old.)

So 1:50-250.

Maybe my understanding of statistics is out? But to me, that suggests that to explain all the deaths as purely statistical, at any arbitrary moment in history, within a three-year window, 50 to 250 inventors in the field of energy efficiency would have to be sitting on a finished, viable, functioning device that is only waiting for its patent to be granted?

In other words, RIGHT NOW 50 to 250 super-energy-efficient devices are sitting finished and functioning on workbenches and only waiting for their dissemination? This seems VERY unlikely. ONE such device would already be quite something, two or three strikingly astonishing. But 250?

Even less likely: IF at any given time, 250 finished devices were waiting for their dissemination — why would none of them ever have achieved dissemination just as a matter of inevitability?!

In other words, if there were indeed so many inventors that their sudden deaths were no statistical peculiarity — why would not SOME of their inventions have spread inevitably?!

Or to put it more succinctly: IF there were so many inventors that their sudden deaths were NO statistic abnormality - then it would be impossible to kill them all to keep their inventions secret...

But that leaves the inverse question: IF all their research were humbug and inviable — why would they die like flies?!

I’m very early in creating a trend model as well as a score prediction model. I don’t have any background in statistics, besides taking a stats class in undergrad.

All trends and data for my score prediction models are from this current season in the NFL and NBA.

For a trend to be “relevant” or “meaningful” how many data points should be included? For example, I have a historical trend that is 70%, but only has 30 data points to pull from. I know 30 isn’t a lot by any means. But the 70% has to be something, right?

As for my scoring model, I’m still honing that in. How often should I adjust it? I have wanted to tweak it every day but I know there is value in letting it run and see how it does over a longer time period.

Hello, my knowledge in this topic is very limited so I have several things I'm not clear with. My dataset consists of State and Year with one dependent variable and a few independent variables. Let's say I have obtained the values for all variables, except for State X where the GDP (independent variable) figures are not available. Is there any way to handle this or do I need to remove entirely State X in my analysis? The GDP of that state is available very recently, starting from year 2024 (which isn't included in my analysis).

I also tried running panel regression analysis using R with missing values on State X and the software automatically removed the observations. I got significant p-values for pooled OLS and FE model, but using Hausman test, RE is preferred. Would RE be a valid model? In previous studies, FE is always a favourable model. How do I choose the best model?

I’m building a website where people pick between two video games at a time. The votes are used to create an overall ranking, with the goal of finding the community’s greatest game of all time.

I started with an Elo rating system, then switched to a Wilson score based on win rate. I'm now considering a Bayesian score or some combination of methods, but I could use some guidance on what makes the most sense based on what I'm creating because I really don't want to overcomplicate complicate it.

Here’s an example of the problem: Star Wars: The Old Republic has won 29 of 32 matchups (about 91%), while Skyrim has won 160 of 196 (about 82%). SWTOR has a higher win rate, but Skyrim has been in a lot more matchups. How should those two records compare in an overall ranking?

Games don’t all appear the same number of times, they face different opponents, and the same person can vote on multiple matchups. I also don’t want a game to suddenly jump to the top just because it passes a minimum vote threshold, which is currently 30 votes

What approach would you recommend for balancing win rate, number of matchups, opponent strength, and repeat voters? Is a Bayesian approach a good fit, or is there a better way to do this? I’d appreciate any suggestions or examples. Thanks!

Hi!

I'm working on my Master's thesis, and my statistical knowledge is limited. My study requires a Hierarchical Multiple Regression (HMR) analysis, which is fine, but I'm struggling with the assumptions [I'm using SPSS].

My sample size is over 500.

1. Regarding normal distribution, I've checked both the raw data and the residuals, neither have normal distribution. My understanding is that, due to the Central Limit Theorem, this should not be any issue and I can continue without transforming, is this correct? Different websites tell me different things and I am very confused now.

2. In general, what assumptions am I supposed to check before the actual HMR? Again, it varies per website. I've seen normality (of residuals), linearity, homoscedasticity, independence of residuals, non-multicolinearity, and non-atypical values.

3. My understanding is that some of those assumptions are checked "automatically" when running the HMR as long as I tick the right boxes. Are any of the required assumptions aside from normality NOT in the HMR box in SPSS? If so, which one? Just so I can keep it in mind and remember to look it up.

4. Are there any other things I should take into account? Corrections I should do, other checks, etc.

5. As I'm asking a million things, I'll add one more. My tutor mentioned the Holm Correction and HC3. What are they, why should I or should I not do them, and at what point of the analysis should each one be done?

Thank you very much!

See original post for full description. Bayesian base rate neglect question.

I have a few thousand audio fragments with bird songs I'm analyzing with a model and Python package called BirdNET. The output is model confidence scores per species and per 3-second window in the recording. A list of different confidence scores is too abstract to analyze directly, so I would like to summarize the scores per species per recording. I'm having trouble picking out a correct method or algorithm, since I'm not that good in statistics.

I added an image of what the data looks like for a single recording. As you can see, BirdNET analyses audio in fragments of three seconds. I use a one-second overlap to not miss any sounds made at the beginning or end of a window. I'm looking for a method that takes into account the following:

• The warbler should have a poor overall score. One good confidence score doesn't mean much when the rest is low.

• The wren should have a good overall score. One poor confidence score should not affect all of the average.

• The buzzard should also have a good overall score. It doesn't call much, but its scores are good. The number of detections should not be a deciding factor in calculating scores, because this will favor birds that have songs of longer duration.

Please note that my goal is not to calculate a probability of presence but recognition strength per recording.

I was told that Bayesian statistics might be a good candidate, but I'm not sure. Some also told me that the overlap will be an issue since part of the same sound is scored twice. So I'm very interested in your assessments and suggestions.

Running into this issue, I was able to find someone asked it already so here it is copied as no one gave them a response.

Example of what I have:

My design is similar to the following in terms of its overall structure: Suppose we are interested in the effects of (IV 1) caffeine intake (high/low) and (iv 2) room temperature (high/low) on how well people do on a test (DV).

Life would be easy, of course, if this was a 2x2 Factorial design where all 4 possible combinations of the two IVs were available (e.g., +Caffeine/Cold room, -Caffeine/Cold room, +Caffeine/Warm room, -Caffeine/Warm room). But for a reason which does not apply well to this toy example, I could not fully cross the two IVs I am working with. So I have +Caffeine/Cold room, -Caffeine/Cold room, and +Caffeine/Warm room, but not the final combination of -Caffeine/Warm room. So not all combinations are possible.

This would not qualify for a fractional factorial, as we cannot subset the data we don't have and don't need to screen the data as we cannot collect more data.

What design would be appropriate here? And importantly, what main analysis could we run?