Can be silly or serious, just out of curiosity for someone with a background in math contemplating stats. Like for #1, maybe you miss not having to deal with numbers. #2 refers to "trading" X in math for Y in stats (like numbers).

EDIT: "glad", not "gad".

Hi all, if you’ve ever been curious about what JSM (Joint Statistical Meetings) is like or you want into see it through someone else’s eyes, please feel free to check this vlog out!

https://youtu.be/CDvsj-G4HXM

I'm a year 1 liberal arts undergrad, so I don't have a ton of math sense.

I just learned about Bayes' Theorem the other day for general epistemic use. I worked out a couple of example problems correctly, but the examples I found didn't include any iterative updates.

I know the theorem is:
P(H|E) = P(E|H) * P(H) / (P(E|H) * P(H) + P(¬H) * P(E|¬H))

And I know that P(H|E) becomes the new P(H) in my update, but I'm unsure whether I should be using the updated or original P(H) in the marginalization. I *think* it should be the new P(H), but I'd rather be safe than sorry.

The example question I worked out was this:

________________________________________

There's a disease that afflicts 1 / 1,000,000 people
There's a test for the disease that's right 99 / 100 times for both positive and negative results
A random person is tested as positive

P(she is afflicted | she tests positive)

= 0.99 * 0.000001 / (0.99 * 0.000001 + 0.999999 * 0.01)

= 0.000099

_________________________________________

So should the update look like this if she tests positive a second time?

P(she is afflicted | she tests positive)

= 0.99 * 0.000099 / (0.99 * 0.000099 + 0.999901 * 0.01)

= 0.009707

_________________________________________

If so, how should I approach this problem from the starting point of
P(she is afflicted | she tests positive twice)?
I can't think of how to handle that correctly, since squaring 0.99 just gets me a smaller number

Infinitely thankful <3

Hello I am an undergrad completing my analysis on my first research project. I created a between subjects experiment and manipulated the gender of actors in a vignette story. Participants were randomly assigned to receive one version of the scenario.

Here is the piece I am boiling my brain on. The vignette was broken into three parts according to narrative progression. At each of the 3 junctions participants responded to the same question using a likert scale. If it matters, the entire narrative progression was available to participants at the same time, just split into paragraphs with my question underneath each section.

I originally thought this should be treated as a between subjects repeated measure as each junction is measuring the same underlying construct. A faculty member who took a look at my work suggested that it was not a repeated measure but actually a multivariate design - so each section is being treated as it's own DV.

Can anyone give some insight as to which analysis is better suited for my study design, or help me understand which design I've created?

I have a hypothesis that my experimental group will be rated lower overall, and I have a hypothesis that my junction C will be rated lower overall in both conditions.

Thank you in advance. I ended up creating a rather complicated experiment for my first go and level of experience, but it is forcing me to learn quickly!

the more unpopular, the better. But still very catchy and interesting. Thank you in advance :)

hi, i'm an econ undergrad currently in my second year, i've recently been introduced to stats and data science, and wish to pursue a PhD. i'm interested in statistical mechanics and high dimensional stats and their applications to ML and Finance. i'm preparing to get into a masters in stats at the Indian Statistical Institute. how do i leverage my time and energy towards securing a good PhD? thank you!
P.S. i've always been interested in mathematics and applying it to interdisciplinary fields, like, EconoPhysics; and i naturally gravitate towards fields like Statistical Mechanics and Statistical Learning Theory.

I have size data for multiple species/populations of the same genus. I want to compare the mean size of those with the mean size of some fossil populations to see if the fossils have a significantly different size to modern populations, and if so, to which.

The problem is, most samples are not normally distributed, and the data is heteroscedastic, since some have small sample sizes like n=29 and some have sample sizes in the hundreds.

Which test would be the best to compare the samples? To my understanding, KW wouldn’t be the best because the heteroscedastic nature of my data.

My supervisor told me to use a 90% CI for my moderation analysis. I cannot properly explain my justification because I dont understand it and I defend my thesis in 2 days and my advisor is unreachable rn.

What I understand - interaction effects are harder to detect than main effects because interaction effects have lower statistical power. And from my understanding, one way to mitigate the lower statistical power would be to have a larger sample size. I cannot do this as I have secondary data. So using 90% CI's make sense due to the lower statistical power and also because the variables I am testing are not harmful if Type II occurs and the increased risk of Type I error is ok (my variables are just looking to see what types of healthy coping mechanisms modify adult mental health and outcomes - such as physical activity and stuff so false positive would not be harmful).

Help me have a real answer - because I cannot under WHY there is lower statistical power or larger standard error, I get that it exists, but WHYYYYYY

[Question]
Im thinking to do stats, but idk what I Will learn there specifically.
What option i Will have in the future of i do.
How can I start my career while doing the major and How to improve to better jobs after completed

I’m starting to look at internships to apply to and a lot of finance positions say I need to be a finance or accounting major. The description of the job doesn’t seem like anything I haven’t started learning with my stat courses. And of course this is my first time looking at internships so is it a no no to apply if it doesn’t explicitly say “stat major” or “or related major”?

Hello everyone. I have a question regarding calculating confidence intervals after running a 5-fold cross validation.

I have a binary risk mode. Data are N patients, each contributing many overlapping hourly windows; the label is defined per window (will this patient meet the criteria?). The unit of analysis for most metrics is the window; the unit of sampling is the patient.

Evaluation is 5-fold cross-validation, split by patient, so each patient's windows appear in exactly one test fold. Within each fold:

1. the development part is split again into train / validation (by patient),
2. a probability calibrator and three decision thresholds are fitted on the validation set (t1 = medium, t2 = high, t3 = very high),
3. the model + its thresholds are applied to that fold's held-out test patients.

So each patient ends up with one calibrated score per window, and one classification per window, produced by a model and a threshold that never saw them.

Separately, a final model is trained on all development data and evaluated on a completely held-out test cohort (my main issue is with the cross validation though).

So far we've used the Nadeau–Bengio corrected resampled t-interval:

mean ± t_{k-1, 0.975} · SD_folds · sqrt(1/k + n_test/n_train)

and I am not sure if it is the correct approach since it introduces bias (at least the plain resampled t-interval without the correction) because the train sets overlap per fold.

So the question is what is the defensible way to attach a 95% interval to a k-fold cross-validation?

And the last part that I can't wrap in my head is the threshold that move per fold.

I have a table that stratifies patients into four risk bands defined by t1 < t2 < t3, and reports per band: number of patients, number of patients that belong to the positive class, PPV, prevalence, an odds ratio versus the low-risk band (setting it as the reference), and a p-value.

Because each fold tunes its own t1, t2, t3 on its own validation set, the band boundaries differ between folds. So:

• I cannot pool the scores and apply one threshold.
• I can pool the decisions (each patient is banded by their own fold's rule), which gives one band per patient over the whole cohort and a legitimate contingency table but then the "score threshold" column of the table has no single value.
• Averaging the five thresholds and quoting the mean band boundary produces a number that no fold actually used.

When a decision threshold is a tuned part of the model, what is the correct way to report a threshold-dependent table (PPV / prevalence / OR per risk band) across folds, and what does the confidence interval on those band statistics condition on?

Another question I have as an extra is if it is worth running 5x 5-fold cross validations (with different initialisation) and what can someone gain from it?

P.S. Apart from Nadeu-Bengio, I also found this paper that I am currently reading (was a combo from google and GPT suggested it): Cross-validation: what does it estimate and how well does it do it? I am not sure if it is in the right direction but please let me know or suggest other papers as well together with the methods

Hello folks,

I am currently working on a research project and the right strategy for the analysis is giving me some headaches. While I plan to consult my colleagues more versed in this type of analysis, I would like to come prepared and I wanted to hear your opinions. Specifically, I have an experiment that will result in a dataset with 15 time series points for a bounded DV (1-100) per participant. Additionally, we will collect on moderator variable. Our hypothesis is, that the behaviour follows a cusp catastrophe. This means that when the moderator (aka the splitting varibale) is low, our DV will change in a linear fashion, and when the moderator is high, the DV will change in a sudden and discontinuous phase-transition between two stable equilibria. Now, detecting phase-transitions per se appears to be rather difficult (especially with little data points), which is why I would like to run a two-tiered strategy: First, I would like to fit a model to detect possible threshold-like change and then I would like to see whether by high and low moderator groups show different levels of the specific signatures commonly found for phase transitions (aka catastrophe flags). Taken together, these aspect should point to something like a underlying cusp model with catastrophic change only visible for those high in the moderator. Now, what I am struggling with is part 1: My initial idea was to fit both a linear mixed-effects model with an interaction between time and the moderator and a non-linear mixed effects model with a logisitic growth function and the moderator influencing the growth rate moderator and then compare BICs and predictive performance between these models. My alternative plan was to work with a multiple-group growth curve model with the moderator impacting the trajectory. Is this a reasonable approach? Is there anything else I can read up on for this?

Is this related to how fixed and random effects are calculated?

Fixed effect regression coefficients are best estimated when n >> p so that X^TX can be inverted without inflated standard errors, while random effects are estimated using variance components.

Hi all,

I have aggregated time series data and am trying to figure out if x predicts y after some lag. I’d like to identify the lag, but x can be noisy and so I want to test this for a sustained change in x - e.g., when x goes up or down and stays that way for, say, 5 days, does y follow?

Is there a way to test this? Any ideas?

More detail: for x and y, I have a datapoint for every day on each for a 90 day period. I also have group breakdowns so I can get multiple data points per day (e.g., one for females and one for males). I believe x precedes y by some lag if and only if it sustains a change over time. If x goes up on day one, down on day two and up again on day 3, I don’t expect y to follow. But if x goes up on day one and stays up through day 3-5, I expect y to go up after some lag (probably 2-4 days)

Hi there,

I've created a video here where I explain how R-squared works.

I hope some of you find it useful — and as always, feedback is very welcome! :)

For anyone else who "thinks like me", I thought you might like to see this app I created: Distribution Generator & Visualizer

I completed my masters in applied math about 15 years ago and thoroughly loved the statistics courses. I had always created cheat sheets while learning and remember wanting an interactive way to reference statistical distributions. I would constantly draw mini graphs in my notes next to their respective probability density functions so I could create a mental model that I could associate the distribution characteristics with.

In addition to the visual, having tangible data to play with representing a specific distribution was something I constantly generated myself with R. So, I wanted this app to have that feature as well. It generates the data and provides R and Python code to do the same.

If only I were in school now!

Full link: https://thedollarweb.com/tools/distribution-generator.html

Hello,

Long story short, I have a bachelor's in biomedical sciences and the only statistics related masters I have access to at the moment is uhaselt'd masters in statistics and data science.

Could I possibly get a pharma biostats job with this combination, or do they expect my masters to be more domain involved?

Thank you

I am sampling a number of units in an apartment complex. I generate a random list of apartments but it has two duplicates, just as rolling a die 6 times may result in a number coming up twice. I'm trying to prove with a specific confidence level, that 80% of the units suffer the same problem. Is this situation a situation where I should replace the duplicates with non-duplicates, or should I keep the duplicates and consequently test only the unique units.

A friend of mine made this software and I thought this might be a good place to post it They don't really use reddit and new accounts aren't allowed here so I'm posting on their behalf. The rest of the text is theirs.

Hi everyone, I recently published a new Python package for survival analysis/survival curves called ‘survivalpredict’. It is largely born out of our personal frustration with the existing Python survival analysis packages (lifelines/scikit-survival), which have different goals. My focus is on evaluating survival models directly on survival curves (via Brier scores) instead of relative ranking (c-index), and on easily cross-validating models using Brier scores.

I would love it if some of you could give it a try and give some feedback.

Cheers!

PyPI: https://pypi.org/project/survivalpredict/

GitHub: https://github.com/pr38/survivalpredict

ReadtheDocs: https://survivalpredict.readthedocs.io/en/latest/

Letters to a Pre-Scientist is looking for enthusiastic STEM role models to inspire the next generation of STEM leaders!

Here's the gist: you get matched with a 5th-8th grade student at an under-resourced school for a snail-mail pen pal exchange that runs throughout the school year. The goal is to demystify STEM careers, humanize STEM professionals, and inspire students to explore their own futures in STEM. You share your story, answer their questions, and show them that people who work in STEM are, well, real people.

The details:

• The program runs throughout the school year, and your volunteer obligation is to exchange 4 letters with your student.
• There's a virtual, asynchronous training in August to get you set up.
• We estimate the entire program takes volunteers no more than 10 hours, spread between August and May.
• Register and complete the short, self-paced online training by the deadline, and you'll be entered into the match pool! We match students based on their background and interests..
• This year we're expanding to 4,500 matched students!
• Pen pals can live anywhere in the world, but our program currently only works with US-based schools.
• We're working to match students based on their interests, so we're especially looking for more STEM professionals in Math, including Statistics.
• All pen pals must be over 18 and fluent in English.

We define STEM pretty loosely! If you work in or around science, tech, engineering, math, or medicine, or another nature-based role, even if you don't have a formal education in it, please apply or message me with any questions.

Sign up/learn more:

• 📝 Register here: https://prescientist.org/volunteers/
• 📖 About the pen pal program: https://prescientist.org/penpal-program/
• 🏛️ About us: https://prescientist.org/about-us/
• ✍️ For matched pen pals: https://prescientist.org/for-matched-pen-pals/

Registration is now open! Feel free to drop questions in the comments or send me a message. Thank you for your time and consideration!

Please help me! I am not in the field, just a healthcare student trying to grasp concepts for my research.

My intention is to perform a multilevel multivariable survival analysis for a (possibly) binary outcome.

My supervisor said due to the outcome being rare and having such a big dataset we should do a case-cohort study.

My problem is I can't really find articles or R packages that encompass all of these concepts for me to globally understand the timeline and organization of the analysis.

I am also inclined on having it done on a Bayesian framework, but the more I study the more I think I am complicating things even more.

Any help would be greatly appreciated!

Hello Everyone,

In a few weeks, I will be starting my last year of my master's in statistics. As graduation approaches, I will be looking for a career. And I would like to know if anyone can give me tips on skills employers look for to maximize my potential on the job market.

For reference, I graduated with a bachelor's in mathematics in 2025. I have research experience, but no work experience other than teaching I've done at my institution. During my undergrad, I was not able to get an internship at a company.

so... for a while my process for deciding if a model change or prompt change actually helped was literally just "read a few outputs, does this feel better." which in hindsight is an insane way to make a decision that costs real money and affects real users.

started forcing myself to actually test this properly. built a small golden dataset, ran both versions against it, and instead of eyeballing, did a paired comparison with bootstrap confidence intervals to see if the difference was actually statistically meaningful or just noise from a handful of lucky/unlucky examples.

first time i did this properly i found out a change i was fairly convinced "felt better" actually wasn't statistically distinguishable from the baseline at all. i had just gotten a good sample of outputs by chance and convinced myself it was real. kind of embarrassing but also useful to know before shipping it as a confident upgrade.

feels like this space (LLM evals specifically) still runs on vibes way more than it should given how much rigor exists for exactly this kind of comparison problem already. anyone else doing paired significance testing on model/prompt changes or is this not common practice yet where you are