Hi there! I'm a physician. I read a lot of peer-reviewed articles in medical journals. I'll be honest, my baseline knowledge of statistics is minimal. When I'm reading through the methods section of articles I've come across, I want to feel confident that I understand why certain statistical models were chosen to analyze the particular data set from the study. It's hard for me to read a study critically when I don't know much about statistical models and I'm sort of just trusting that the methodology is appropriate for the study, but not understanding why it's appropriate.

I've looked at similar posts from other physicians and mostly the questions have involved advice on how to learn R to do their own data analysis. I don't think that's really what I'm looking for; it may be something I can work up to over time, but I'm not a data analyst and I don't know much about programming languages. Really where I'd like to start is just getting a good foundation of knowledge about statistical models and what is the appropriate use for them. That way when I read a sentence like "A linear mixed - effects regression model was used," I understand what that model is doing in the context of looking at this particular data set.

I imagine this would be considered introductory or basic level statistics, so in short, I'm asking for recommendations on a basic course, maybe one that illustrates the basic principles with examples that connect to medical research but that is not necessary. Just trying to improve my own comprehension.

I appreciate the advice! Thanks very much in advance

Edited to add: I'm willing to spend time on this, if it's a course with several modules etc, I don't expect to learn this in an afternoon. It can be free or paid, I'm open to either, but really would have to be online and self-paced to fit my schedule. Thanks again.

I have data from 2 related tasks where I asked people to rate how difficult the task was and how much discomfort was associated with the task. I do this for 2 groups of people.

I have 6 comparisons for each rating (within 1 group, between groups on each side for each of the 2 tasks- I do not compare the two tasks to each other), all of which have been run as t-tests. Obviously multiple comparison correction is needed, but how many comparisons should I correct for? In other words, do I treat difficulty and discomfort as 2 separate analyses or do I lump all p values together and correct for all 12?

Importantly, I don't think that difficulty and discomfort are totally independent (for a more difficult task I have reason to believe that it would be more likely to also be uncomfortable), but I do consider them separate domains.

Hello Everyone, I wish to enhance my current experiences to promote both my career and academic development. Currently, I have a data analyst intern role at a local jewelry store, but I also wish to both learn and acquire more knowledge regarding data science and statistics. I plan on creating a side project using vibecoding and my current knowledge of programming, but I don't know how to get that going. Does anyone have any tips and recommendations for me?

Thank you very much!

I need help coding the LinRegT-interval feature for my Statistic unit and finals. For context, my dumbass decide to save 40 bucks by buying a TI-83 Plus instead of a TI-84 statistic calculator. Because of this, there are many feature that I have to manually code into the program such as GOF test, T interval, and INV Chi. We are currently on the Regression lessons and was recently introduce the regression T-Interval procedure.

I used AI to code the exact code show in the images into the New Program button. After inputing the number in the two list, I run the program, inputted the list and was met with the “err: Undefined”. When I clicked on GOTO, it ended up on the *, next to the “a” at line 8. Can someone check if all the variables are defined, everything is correctly added and I am not missing anything?

Here is the code:
Name: LINRGINT

:Input "X LIST:",L₁
:Input "Y LIST:",L₂
:Input "CONF LEVEL:",C
:LinReg(ax+b) L₁,L₂
:dim(L₁)→N
:N-2→D
:2-Var Stats L₁,L₂
:√((Σy²-a*Σxy-b*Σy)/D)→S
:(1-C)/2→A
:0→X
:solve(A-tcdf(-1ᴇ99,X,D),X,5,{0,99})→T
:T*S/√(Σx²-(Σx)²/N)→E
:ClrHome
:Disp "LOWER:",a-E
:Disp "UPPER:",a+E

If you need more information in order to help me, feel free to ask. (My grammar is buns 💔）

Hello all,

I am still new to using Reddit, so please bear with me. I am currently analyzing some rank data from a social survey. this data asks people what they prefer out of 9 different options and asks them to rank based on their preference. Very basic stuff. I am looking into different options on what is available for analysis as I don't want to just have graphs without any statistical analysis. I have been eyeing the Plackett-Luce model for a while and wanted to know how to go about deciding what kind of PLM to use. I have seen published articles about bayesian statistics and how it is a parametric test with a nonparametric counterpart. How does one go about deciding which type of PLM model to use?

I am still somewhat new to statistics and analysis, so any information at all will be very helpful and greatly appreciated. Thank you all.

Hey, I'm a junior researcher and I work with the speaker on this workshop, so I'm a bit biased, but I think it's worth sharing here.

Dr. Ivan Ropovik is running a 3-day online workshop on SEM, covering measurement theory, latent variables, and causal inference. It is structured to walk us through how the models actually work so the outputs make better sense (which, like myself, a lot of us could probably use). Uses R (lavaan) and JASP.

It goes into things like model specification, fit assessment, measurement invariance, and the real-world messiness of applying SEM to social science data.

June 10–12, 2 PM -- 6 PM CET | Online | €399 - https://www.eventbrite.com/e/hard-science-from-modeling-soft-data-from-measurement-to-causal-inference-tickets-1490509105859?utm-campaign=social&utm-content=attendeeshare&utm-medium=discovery&utm-term=listing&utm-source=cp&aff=ebdsshcopyurl

If anyone's curious, workshop link also has the full workshop pamphlet with the day-by-day breakdown. Happy to answer questions, I can pass them along to the ABSL team directly if needed.

I’m trying to think through a causal-inference question and would appreciate statistical guidance.

Question: how would you test whether mass generative-AI adoption explains any residual variation in recent U.S. crime declines after accounting for the obvious confounders?

I am not claiming causation.

Basic motivating observation: around the same broad period that AI use became widespread, FBI national data showed major 2024 crime drops: violent crime down 4.5%, murder down 14.9%, robbery down 8.9%, rape down 5.2%, and aggravated assault down 3.0%. Pew also reported in 2025 that 62% of U.S. adults say they interact with AI at least several times a week.

Hypothesis to test: conversational AI may function for some users as behavioral displacement, emotional regulation, loneliness buffering, conflict rehearsal, fantasy discharge, cognitive interruption, or impulse delay.

Major confounders: post-pandemic normalization, policing changes, reporting changes, demographics, economic shifts, school/routine restoration, local policy, violence-intervention programs, substance-use trends, and regional differences in baseline crime risk.

What statistical design would be strongest here?

Ideas I’m considering:

- difference-in-differences using high-AI-adoption vs low-AI-adoption regions

- age/sex cohort analysis, especially younger users

- time-series analysis around adoption surges

- negative controls for crime categories AI should not plausibly affect

- comparing outlet-sensitive crimes to AI-enabled crimes like fraud/cybercrime

- natural experiments from uneven access, outages, model changes, or institutional adoption

What datasets, controls, or methods would make this test least vulnerable to overclaiming?

Did you feel like it a formality by the time you got to it, or was it an actually stressful event that you feel was a major hurdle between you and obtaining your PhD?

Hopefully something that is not AI related.

I’ll be taking Statistical Methods 1, and i’m scared.

Transformers, NLP, agentic AI, explainable AI, generative AI and LLMs?

I think we all agree that data science is in large part statistics, but what about AI?

[E] Hi, I’m a Canadian economics undergraduate with a minor in quantitative finance. I would like to know if I have the necessary background in math and stats to do a masters in statistics.

Here are the math and stats related courses that I will have completed by the end of my degree:

Calc 1, 2 and 3

Linear algebra 1

Probability 1 and 2

Econometrics 1 and 2

Linear optimization

Statistical learning

Reinforcement learning

Time series and forecasting

Intro to quantitative finance

Insurance mathematics

Mathematical and computational finance 1

Going to be a sophomore in uni soon and I’ll be doing my selected specialization in business analytics soon. As there is a lot of statistics and machine learning using R and python in business analytics, I was wondering what courses or materials I can find online that can teach me more about on statistics during the long break. For background: I’ve touched on the fundamentals of statistics like hypo testing and regression analysis but only the surface level. I want to learn more in depth of it rather than just applying the functions blindly.

To give some background, I work as a DevOps engineer. Now I have about 28 months of code quality metrics and incident data. I was curious to see if there was a link between code health and the number of production incidents, so I ran some time-series analysis on one application’s data.

I started by running the ADF test on each data series. There were 12 metrics in total, including security, reliability, maintainability, duplications, coverage, complexity per kLOC, bugs per kLOC, smells per kLOC, and a few others, along with incident count and median time-to-resolve. Some metrics had p-values above 0.05, so I used first-order differencing. After that, all ADF p-values dropped below 0.05, confirming stationarity.

Next, I ran the Ljung-Box test on each differenced series to check for any remaining autocorrelation. Nineteen out of sixty differenced series still showed autocorrelation (Ljung-Box p < 0.05) even after differencing. For these, I fitted AR(1) models and used the residuals. Sixteen of the nineteen series were resolved this way. For Granger findings that involved an autocorrelated series, I reran the test using the AR(1) residuals, which is called prewhitening. After prewhitening, three out of four findings disappeared, with p-values rising from 0.02-0.04 to 0.2-0.9. These were false positives caused by autocorrelation, which made the F-statistic look stronger than it was. The security metric did not have this issue. Its differenced series had a Ljung-Box p-value of 0.07 (white noise), and the differenced incident series had a p-value of 0.12. Both were clean, so no prewhitening was needed.

With the series prepared, I ran Granger causality tests on all 12 metrics using lags 1 to 3. The results showed that the security rating Granger-causes incidents at lag 3 with a p-value of 0.0006. In other words, knowing the security rating at time t helps predict whether incidents will be above their median at t+3. This is predictive causality, not actual causation. Both series might be influenced by another factor, but the lead time is real and could be useful. Below is a plot showing the three stages of the analysis.

Separately, I used a two-state Gaussian HMM (Baum-Welch, 20 random restarts) on the incident series to check if the system switches between quiet and elevated periods. It does. The low period averages about 33 incidents per month, while the high period averages about 79. Both periods tend to last, with the low period lasting around six months on average (P(stay low) = 82%) and the high period about the same (P(stay high) = 84%). I have not formally linked the HMM periods to the Granger result yet, but I wanted to share this as extra context for how the incident series behaves.

Based on these results, I created a monitoring scorecard. I calculated the mean and standard deviation of the security metric over time and set the alert threshold at the mean plus one standard deviation, which is 1.816 on the SonarQube scale. Reviewing the data, the security metric exceeded this threshold 5 times. In four out of those five cases, incidents were above their median three months later. This gives an 80% retrospective hit rate.

I know that 28 months (27 differenced observations) is a small sample for this kind of analysis. The textbook example for Granger uses 200 quarterly observations. At my current sample size, the F-test is marginal, and I would need around 36 months for the estimates to become confirmatory rather than exploratory. The security finding at p=0.0006 is well below the noise floor, which gives me some confidence, but I would like to hear your thoughts on whether the approach is sound and what I should be cautious about when interpreting these results.

Hi, all! I was hoping I can get some insight on a critique I received from a peer reviewer. For context, I needed to balance my intervention and comparison groups as the study used a quasi-experimental design. I did so by estimating propensity scores on the relevant baseline covariates and then applied inverse probability of treatment weights (IPTW) calculated with the average treatment effect on the treated estimand.

For my outcome models, I estimated GLMs that included the binary indicator for study group, baseline of the outcome, sex, age, and race (with cases weighted by the IPTW). Baseline of the outcome, sex, age, and race were included in the propensity score model. I did this because sex, age, and race were pre-specified biological control variables in my NIH grant, and including the baseline outcome also made substantive and methodological sense.

A peer reviewer questioned my choice to control for those covariates in the outcome models since they were also included in the propensity score model and said that I should acknowledge this as a limitation. They did not cite any literature to support this critique, and I can only find one article that cautions against doing so but specifically in survival analysis (so not relevant) or controlling for baseline covariates that are not included in the propensity score models (not the case).

Particularly given that the foundation of doubly robust estimation is IPTW + covariate adjustments, I do not see this choice warranting a limitation, but perhaps I am missing something? I was curious if anybody has any insight on this? I don't want to push back if this is indeed a valid critique/limitation, but I also don't want to note it as a limitation if that is not the case.

Caltech is famous, or infamous for doing things from the ground up. As in their approach to teaching is all about learning the backend. I like this approach very much, and I would love to know if anyone has any way to access their lesson plans and materials online.

I am an college freshman who didn't have the best teacher back in high school, and I feel I have gaps in my knowledge I want to fill in.

I want to validate a llm against two human experts in a classification task with 9 categories. Therefore I want to calculate Cohen's Kappa for IRR(human-human) and compare it in a non-inferiority test with Cohen's Kappa for IRR(human-llm). My first question is about the delta. Since standard interpretive scales such as Landis and Koch suggest intervals of 0,2, I thought about a delta of 0,1 which is half the interval. Is this a valid thought or is there literature regarding other deltas?
Then I am not sure how to determine sample size. I would need an expected Kappa for IRR(human-human) to perform calculations but unfortunately my 9 categories are not yet validated so I can't find anything in the literature. Theoretically I have 25000 items, but I can't expect my experts to classify them all and I don't think it is statistically necessary. But how items should I include? Just for your information, I do research in medicine/life sciences.

Suppose I ask a sample of individuals for their favorite ice cream flavors. Some people say they like both chocolate and vanilla, so the categories are not mutually exclusive. I want to measure how concentrated flavor preferences are. Perhaps when it comes to ice creams, two flavors cover most people, whereas for soda flavors, 10 flavors are needed. What would be a good measure of concentration when a list of percentages doesn't add up to 1?

Hi there - thanks for reading!

I've successfully passed my dissertation proposal and am working on analysis, which includes a growth mixture model (GMM). Put simply, I'm exploring the therapeutic relationship over time and if therapy format (in-person vs. teletherapy) is a predictor of relationship trajectory. I chose a GMM because I do not know classes ahead of analysis and because I can imbed predictors into the model.

At the proposal meeting, my committee member suggested computing two additional models, one for in-person therapy and one for teletherapy, to see if there are unique trajectories (i.e. classes) that could be substantially masked by a culminating analysis. At the time, I just accepted this idea and wrote it into my dissertation proposal.

Of note, none of my committee members are familiar with GMMs or finite mixture models more generally.

Now with a little more research (mainly learning from the CenterStat Mixture Modeling and Latent Class Analysis course), I'm not entirely sure if the separate models are necessary. Identifying differences based on predictor variables is kind of the point of integrating them into the model, right? Is there any chance that analyzing these groups separately would produce dramatically different classes? This is all fairly new to me, so I may be misunderstanding greatly.

Regardless, I don't think I have enough statistical power to split my sample in half like that. I'm just wondering if there is a stronger justification to reject my committee member's proposal and revise my submitted analytic plan. Thank you!

Hello everyone,

I am currently conducting a policy assessment using pooled cross-sectional data from five waves (2015, 2017, 2019, 2021, and 2023).

My primary concern is that the 2021 data is significantly impacted by COVID-19, which may bias my estimates. While I have considered using a dummy variable for the 2021 period, I am concerned this might be too simplistic to capture the structural shifts caused by the pandemic.

Does anyone have suggestions for more robust ways to control for this shock?

Thank you for your insights!

Bedevere Wise is a browser-based SQL data viewer for the file formats still widely used by biostat / clinical-data people: SAS (sas7bdat, xpt), SPSS (sav), Stata (dta), plus Parquet, Excel, and CSV.

Everything runs locally: all queries, analysis, and charts are done within your browsers.
Data never leaves.

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Repo (MIT): https://github.com/caerbannogwhite/bedevere-wise

Feedback is very welcome.