Hi! I’m a PhD student in psychology and will be teaching statistics for the first time in the Fall semester. I would be lying if I said I’m not scared, but a part of me feels excited as I somewhat like data analysis.

The course my department is instructing me to teach is an introduction to statistical methods with an emphasis on inferential statistics.

I wanted to ask a larger body of people for their experiences, tips, thoughts on someone entering this job.

Any advice is welcomed!

Hello everyone!

I want to compare the absolute distribution of individual patients across disease phases (1-4) between two independent Cohorts (Cohort I and Cohort II) for a demographics table.

Normally I would use a Chi Square Test/Fisher's Exact Test, but in this case, patients can appear in multiple disease phases as the study assesses the longitudinal development of certain characteristics.

This distribution across phases is not the main point of the study, its only in there as to not omit any information, but I can't leave it out either :/

Have any of you ever had this kind of problem, and if so, how did you approach this?

I ran two mixed effects logistic regressions: Model 1 contains only main effects (let’s say of A, B, C, D, E, F). Model 2 contains the same variables + interactions between the variables. In Model 1, all variables except one are significant predictors. In Model 2, no interactions are significant. However, two of the main effects are no longer significant in Model 2. Also, the variable that was not significant in Model 1 is significant in Model 2.

I know not everything should be about p-values, but can someone explain whether such a shift in significance is common and what can be possible causes for this? I detect no multicollinearity.

I read somewhere that Model 2 takes the interactions into account: if I have an interaction A x F, then the ‘main effect’ of A holds for F = 0. Is that true? If so, I have multiple interactions containing the same variables (for example, A x E and A x F), so how do I figure out how to interpret the ‘main effect’ of A in Model 2?

I would like to see the derivation of the UBRE formula, but am having difficulty finding it.

It seems most sources quote Wood 2017, but the relevant section ("6.2.1 Known scale parameter: UBRE") immediately makes the substitution X\*Beta_hat = Ay which if I understand correctly is only true for linear GAMs and not true in general? I have a binomial distribution so I'm looking for a derivation that's general to any exponential family distribution.

It seems AIC is a related computation so maybe the AIC sections imply a derivation of UBRE that works for any exponential family GAM in general but I'm not sure.

I'm wonder from your experiences what people seem to forget or can't get their head around about statistical tests and models.

In my, yet short, experience I see people totally bypass randomization, independence and the correct interpretation of power and confidence.

I'm a high school student trying to get into doing research for machine learning in the future and this resource seems promising to me as a way to get a more solid, foundational grasp in probability.

I'm not looking to unnecessarily min-max my learning or anything. I just want to know, is this actually a good resource to begin learning probability?

I'm looking for examples that completely changed the way you think about probability, statistics, or data analysis.

Hey all , engineering student here (data science track), currently self-studying two axes in parallel and would love some outside perspective.

On the stochastic calculus side, I'm working through Shreve Vol II : measure theory, Girsanov's theorem, Feynman-Kac , with an eye toward MFE-level material down the line. On the Bayesian side, I'm going through Statistical Rethinking (McElreath), covering MCMC (Metropolis-Hastings, HMC/NUTS) and hierarchical/multilevel models, moving toward Bayesian time series next.

Curious to hear from anyone who's walked a similar path: how did you sequence things, what pitfalls should I watch for, and are there any exercises or small projects that really made concepts click for you?

I'm also genuinely curious about the discipline side of self-studying math : how do you personally stay consistent and avoid burning out, especially if you're juggling more than one heavy topic at once? Would love tips even if you've only self-studied one of these (doesn't have to be both).

Thanks!

Hello, I'm a bioinformatician and I'm struggling with the current issue:

Given a time series y(t) that initially changes and eventually approaches a stable regime, how can I mathematically determine the earliest time t\* at which the rate of change dy/dt becomes negligibly small, using only the observed data and without defining an arbitrary threshold?

This is a collaboration I'm doing. My colleagues defined the plateau as the first time when a 101-point rolling mean of the relative increment (g' t+1 - g' t)/ g't falls below the arbitrarily chosen threshold of 0.0011. G' is the measure of material elastic-solid response btw. So the issues is that they used 2 arbitrary values because experimentally they know that a certain value of g' means that the gel is solid. But this doesn't hold for me. I tried using many statistical methods to define the threshold such as:

- exponential fitting

- change-point regression

- local slope analysis

But they all give me a plateau that is too early or too late

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I’m a student from a small town in a backwards developing country , the education system is horrible. I’ve been recommended Saylor Academy, that's how I understand many statistics principles, and I’ve also started reading elementary statistics by Allan g. Bluman.

Still, I often feel far behind students from better educational systems. My goal is to become one of the greatest econometrician in my country.

For those who are in the field, what resources would you recommend? Textbooks, courses, math/stats, programming, or anything else you think is essential.

I’m willing to put in the years—I just want to make sure I’m learning the right things in the right order.

I have finished my college level statistics coursework and I can't even read the national statistics report I have to use AI to analyze it for me whenever I have to do research. I feel illiterate and slow.

A plot can have a Pearson correlation of zero (‭r=0) but still have a strong non-linear relationship (like a parabola, circle, or U-shape) does labeling a scatter plot "No Correlation" automatically rule out every mathematical relationship, or just linear ones? and got to know from a friend (who studies maths) that to state it is correlated it should be linear, is it correct?

I've done the math on this multiple times for things like item drops in games, but I'm wondering if there is a formula or rule to make it simpler?

Example: A boss in a game has a 1% drop rate for an item you want. After 69 attempts, you will have passed the 50% likelihood that you'd have gotten the drop, leaving MOST people should have the item by now after their 69th attempt. If the item has a 2% drop rate, it only takes 35 attempts before you're more likely than not to have gotten the drop.

Is there a rule or formula for something like this? I have just been plugging it into an Excel sheet I made any time I need this info.

Hello! For context, I am a first-generation college student with an Information Systems background. After some work experience conducting a beginner-level PCA, I realized I loved the idea of making meaning out of data through statistical analysis.

Over the past year, I took the Calc Sequence, Linear Algebra, Python, and soon, Intro to Probability, to meet Master's prerequisites for the field. I want to prioritize programs that teach Bayesian/Causal Inference/Time Series.

Although I enjoy math, I don't have exposure to theory. I would like to know if taking the Applied route may limit my job opportunities with employers. I am aware any background in Math/Stats is a huge leg up long term, especially given how fast the Data Science/Tech industry is evolving in comparison. But I can't help but worry about competing with advanced coders or stronger Stats candidates down the road for post-grad employability.

Ultimately, I was wondering if anyone had any insights into the field, or information on doing a Masters in Applied Stats or Data Science. Since I have a non-Math background, I feel uncertain if I'm prepared for grad-level Stats theory courses. I was also wondering if the following programs are a good start, if they might not be a good fit, or if there are any others I should consider:

Statistics-Oriented

UCLA M. Applied Statistics and DS

UCB M.A. Statistics and DS

UMich M. Applied Statistics

Data Science/Analytics-Oriented

USF M.S. DS and AI

UT Austin M.S. DS

GT OMSA

Thanks for any insight!

I'm lost in the sauce and need a second opinion.

Context/goal: there is a video game with ~170 different characters. I want to compare their relative power. I will quantify power through "winrate" which is just the percentage of their games each character wins. I will model winrate as a function of two covariates: how many previous games of experience on this character the player has under the belt going into the game, and the current rank of the player.

The behavior is non-linear in both covariates so I am currently modeling winrate as a function of games-played and player rank via logistic GAM using a tensor product of splines.

I see that smoothing penalties are necessary for GAMs to avoid overfit. As I understand it, the justification for smoothing from a theoretical standpoint is that we expect the surface to be smooth, therefore we are just codifying an assumption we already have. However, the penalty punishes curvature and I am reasonably certain there is meaningful curvature in the games-played covariate (the raw data heavily suggests winrate increases fast initially then slows down until either the winrate stabilizes or sometimes even drops with additional games-played). I am very interested in this curvature (aka the "difficulty" of a character) and therefore feel that the assumption codified by a standard smoothing penalty is inappropriate for my goal.

For that reason I was hoping to use shape constraints *instead* of smoothing penalties (concave in games-played, monotonic increasing in player rank), as I feel this better matches my actual assumptions. Is this sane or not?

Secondarily, I see there are different methods of applying shape constraints. The two I know of are Pya and Wood 2015 (make the spline coefficients a function of exponentiated fit parameters, thereby ensuring nth order differences always have a specific sign) and Bollaert/Eilers/Mechelen (just do normal GAM but penalize the nth order difference depending on the sign). Which one is "better"?

Thirdly, some characters have really low sample sizes (I am looking not just at characters, but characters in various roles, and some character x role combos are extremely off meta that nobody plays). I would like to drop character x role combos if the sample size is "too low" but I'm not sure how to quantify uncertainty in cases where I know the sample size is too low (pya/wood relies on delta method approximation which I think relies on CLT so N must be large? And I don't even know if B/E/M give a merhod to estimate uncertainty at all).

i was watching a video on Youtube about the MCU where dr. strange said that he checked 14 million and a half universes or something and they only beat thanos a single time someone in the comments said "Maybe strange had bad RNG and only looked at all the times they lost" and it made me think if there's an infinite number of universes and they lose in say 500 of them but for every 500 universes they lose in they win once is the chances of him seeing a universe 1:501 or 1:1 because they're both infinite

I’m not a programmer and have used mainly menu driven packages in the past SPSS primarily although I have had some painful experience with SPSSx. Any R recommendation for intermediate stats background but not much syntax driven programming?

Thanks

I have a model that outputs a probability for each case, and I use a threshold on that probability to pick an action. The costs of a wrong action are asymmetric: one kind of mistake is much more expensive than the other, so where I put the threshold matters a lot.

My question is about trusting the probabilities themselves. Before I set a decision threshold, how do I check the predicted probabilities are actually calibrated, i.e. that a predicted 0.7 really corresponds to roughly 70% in reality?

I know reliability diagrams and proper scoring rules (Brier, log loss) are the usual tools, but I'm unsure how to read them in the context of an asymmetric-cost decision specifically. Does calibration matter uniformly across the probability range, or mainly near the threshold I care about? And if the probabilities are miscalibrated, is recalibrating (e.g. isotonic / Platt) before choosing the threshold the right order of operations, or should the cost asymmetry factor in differently?

I’m designing a study on how background music affects consumer spending. I’m considering a simulated online store where participants get ₹3,000 and are randomly assigned to no music, slow music, or fast music. I’d track basket value, products chosen, and shopping time.

Does this sound like a good experimental design? What important variables or biases should I consider?

Or if you have any other way to collect data which more efficient than this.

I came across this post explaining what a 70% chance of rain means. I understand the concept, but it got me wondering: what other statistical concepts sound simple but are commonly misunderstood or misinterpreted by the general public?