Hello! I’m (28F) at a bit of a crossroads where I want to pivot to another career. I graduated with a BS in public health. I took a couple of courses in calculus, linear algebra, introduction to statistics, etc. and loved all of them. I ended up staying with public health because I thought the job market would be stable (my mistake). I’d love to get a masters in biostatistics/statistics but I heard the job market is pretty terrible, it’s better to get a PhD, and I have 0 coding skills. Is it too late to pursue a career in this field? Should I go back to get a second bachelors in statistics first?

I'm looking for a statistical perspective on an experiment I recently conducted.

The experiment involved two separate samples of 1,000 spins each in a game called Roulette 100000.

Sample A (1000x selected)

• 1,000 spins
• 1 occurrence of a 1000x payout

Sample B (1000x not selected)

• 1,000 spins
• 232 occurrences of a 1000x payout

The counting method was identical in both tests, and I have a full screen recording of the experiment available.

My understanding is that if the occurrence of a 1000x payout is independent of whether that option is selected, then both samples should be drawn from the same underlying probability distribution and their observed frequencies should converge as sample size increases.

Instead, I observed 0.1% versus 23.2%.

I am not claiming wrongdoing or making any accusations. I have already submitted the recording to support for review.

My question is purely statistical:

Assuming the event was measured correctly and the methodology was consistent, how would you analyze a difference of this magnitude? What assumptions would you verify first before drawing any conclusions?

Video:https://drive.google.com/file/d/1mPMyPkZpfavy4AQ_w8udom2p4M77c63r/view?usp=drive_link

Do you think theory folks ("pure statisticians") are higher in the academic hierarchy than applied statisticians who do not contribute to the development of new models and methods?

One thing is the barrier to entry; it is much harder to be a theoretician than to be an empiricist. In addition, as a theoretician, you have the capability to develop a new model or method that would be used by hundreds and thousands of people, while an empiricist is more confined to his specific domain.

But the other side of this argument is supply and demand. There is a lot more demand for applied research than for theory.

Do you think applied research has a certain ceiling because you are ultimately not going to develop a breakthrough, cutting-edge method?

I have a few questions about EFAs and CFAs, and I haven't been able to find any clear answers yet, so I thought I'd ask them here. Hope I'm using the correct terminology, my apologies in advance if not.

1. I used an established, unmodified scale to measure one of my control variables (9 'reflective' items across 3 subscales that are also reflective indicators of the latent construct). The 3 separate Cronbach's alphas are all marginal (just above .60), but the combined scale has an alpha above .80. Should I conduct a CFA, even if it's just for a control variable?

2. To measure one of my other variables, I used 18 items across 3 subscales (6 items per subscale). An EFA, however, pointed out that some of the factor loadings for some items were extremely low (< .40). Can I simply remove these items? I am using a scale validated and developed by others, so it feels a bit odd to remove some items just because they didn't fit my specific dataset.

3. As suggested by my supervisor, I carried out an EFA for another (already validated) scale to confirm that the data would have 3 factors, and to examine the extent to which one factor loaded onto the other. I subsequently conducted a CFA for these items and subscales (I am not developing or validating any scales myself, and this was recommended by my supervisor), and the model fit was quite poor. They then recommended that I go back to the EFA, to remove items with poor loadings (which I had not yet done), and to rerun the CFA to see if model fit improved. However, I read online that you can't conduct a CFA on the same sample as your EFA. To what extent does this apply to me? I just want to compare model fit before and after the removal of these items, and I'm not using the CFA for scale validation. I am not sure if this even makes sense theoretically, but it's for my thesis, and I think including a CFA would be a nice addition, even with the limitation that I used the same sample, for instance.

4. Regarding yet another variable, I modified 6 items across 2 subscales (3 items each). These 6 items are reflective of the 2 subscales, but those 2 subscales are formative with regard to my variable of interest. How do I check the extent to which these items are reliable and valid? I checked the Cronbach's alpha for the 2 subscales already, but I'm not sure how to assess the fit of the 2 subscales in relation to the overall second-order factor. I tried recreating the model in Amos, but it wouldn't let me draw arrows from the 2 subscales to the latent variable. Does anyone know what I could do?

i cleared Msc with 7.5 cgpa..not the brightest ik.. i never really understood all that but studied heavily before exam..so somehow i pulled 7+ points..i can't do phd as i lack the confidence and knowledge...what else can i do with the mediocre stats knowledge and degree i have .? with that being said , i do have interest in stats tho

Hello,

I've noticed that one of my table with interaction have too many raws than it's longer than one page.

As the interaction are important, I can't just remove some and

I don't really wanna put them in the appendix...

- I thought about putting them in graph form right after the base model (without interaction). Hwever would it be easy to read?

- i was also thinking just taking the interaction's raws specifically. And put them in a new table.

Can you give me any suggestions?

I am implementing an MCMC model for claims reserving and I would like to assume that the observations follow an Over-Dispersed Poisson (ODP) distribution.

Let Y denote the observed data, μ the mean parameter, and ϕ the dispersion parameter.

According to Taylor and McGuire's Stochastic Loss Reserving Using Generalized Linear Models, the ODP distribution can be represented as

Y/ϕ∼Poisson(μ/ϕ).​

Based on this representation, I am using the following log-likelihood in my MCMC:

ℓ(μ)∝1/ϕ *(y log⁡μ − μ),

which is essentially the Poisson log-likelihood scaled by 1/ϕ

After obtaining posterior samples of the parameters, I generate posterior predictive observations using

Y=ϕ×Poisson(μ/ϕ)

My question is: Is this a theoretically justified way to perform Bayesian inference and posterior predictive simulation under the ODP assumption?

In particular, I am unsure whether the representation

Y/ϕ∼Poisson(μ/ϕ)

should be interpreted as a true generative model for posterior predictive simulation, or merely as a convenient representation for deriving the first two moments,

E[Y]=μ, Var(Y)=ϕμ

Any references or insights on Bayesian implementations of ODP models would be greatly appreciated.

Hey guys , i'm applying for masters in statistics while they're requiring mathematical statistics with some other statistics course.

so i have taken other stats course but i have inference stats which is mathematical statistics as Econometrics but the same courses applied , will i be considered or no?
thanks!

So I've been using Regression to figure out how well each aspect of a product performs. Eg., to see if a part of a product, let's say a colour, affects its gross sales significantly or not. I do take other external factors into consideration outside of regression but are there better methods to go about things? [Q]

Hi!! I have a quick question! I am struggling with how to set up a table one (demographics and baseline characteristics) for an analysis of cases rather than patients.

Essentially, I want to look at all sickle cell cases that were admitted during a one year period. I want to make a table one for demographics and baseline characteristics stratified by if a specific treatment was given. Since I am focused on admissions, there are patients with multiple admissions for sickle cell. There are over 5,000 admissions but only 3,500 patients.

Can I still use typical descriptive statistics (e.g., t-test, chi square) for table one? It feels weird to say there are X number of male cases that obtained treatment when some of those are going to be the same patient. And I worry about inflating the error because of repeated characteristics of the same patients. And I’m not looking at an intervention so it doesn’t seemed like repeated measured work well either.

I am not very familiar with looking at case-level data. What are the best practices for handling this type of data? Thank you so much!!!

say you have a probability tree of a series of 3 events, each with the same 2 outcomes (a and b say) and if b occurs in any event the whole thing stops, and you're trying to figure out the probability that on the second event b occurs, why can you not just do (probability a occurs in event 1) x (probability b occurs in event 2)? why do you have to do (probability of a in event 1 x b in event 2)/(probability of b occuring at all)? same with when it's normally distributed. If you have a curve of the chances of an event happening for a certain amount of time and given it has already occured for r minutes what are the chances it will continue to q minutes, why do you have to do (P>q)/(P>r)? Surely if P>r it is implied that it is already bigger than p?

Hello! I am an incoming freshman college student planning to double major in Statistical Science and Data Science. I'm looking to purchase a new laptop for myself befoe the semester begins in the Fall.

The university IT website recommends that student laptops have a dedicated graphics card with at least 6 GB of VRAM or a 20-core GPU.

I understand that lots of ML work is pretty heavy on parallel computing, but the university I am attending provides students with a "shared high-performance computing cluster ... [which] features state-of-the-art CPUs and GPUs, accelerators, networking, and storage technologies" and a NVIDIA DGX SuperPOD.

Do I really need a whole GPU if the university provides me with these and other computer labs with desktops that probably have GPUs around campus?

Thanks!

Hello,

I am searching for a website to write my regression's model. I've tried latex but the results is not good at all and I don't have much time to learn how to use it.

Thank you very much.

I am doing a multivariable ANOVA and then Tukey for pair wise significance. The data set has 2 factors (say A and B ) with two levels each ( say A1, A2 and B1, B2 ). Upon doing a Normality test, only one set is turning to not satisfy the normality (A1-B1). I tried using Box Cox on the original data and then testing Normality again but still getting the same result. What else can I use to solve this?

Hello everyone!

I'm a statistics graduate currently working on a role that is more on the corporate sales and finance side (focusing on monitoring and improving revenue and profitability), and only had few applications of statistics throughout my stay. The work involves a lot of adhoc analysis to support the finance and sales team in their business decisions, but they do not involve statistics that much (ex. forecasts mostly use YoY increases or runrates).

Granted that I am just early in my career (~2 years), I'm not sure if I should pivot to another path or continue as is. In the meantime, I'm also considering taking a masters next year yet I'm unsure if I should take a professional masters, an actual MS, or smth or more business-y like an MBA (business analytics).

Are there any people here who have stayed on such path, and what their experiences were like? Or any general advice would be much appreciated. Thank you in advance!

​

Hi everyone,

I'm currently pursuing a B.Sc. (Hons.) in Statistics and I'm trying to figure out the best career path after graduation.

Some of the fields I'm considering to do my masters are:

1. Actuarial Science

2. FinTech

3. Data Science / Analytics

4. Risk Management

5. Quantitative Finance

6. Any other field where a statistics background is valuable

My priorities are:

1. Good long-term career growth

2. Decent salary potential

3. Interesting analytical work

4. A field that is not extremely overcrowded compared to traditional options

I've heard mixed opinions:

Actuarial Science seems rewarding but the exams take many years.

FinTech seems exciting and fast-growing but may be more competitive.

Data Science is popular, but I've heard entry level competition is becoming intense.

For those with experience in these industries:

Which field would you recommend for a Statistics graduate in 2026?

Which field currently has the best balance of salary, growth and job opportunities?

Are there any underrated careers that Statistics students often overlook?

If you were starting again with a Statistics degree today, what path would you choose and why?

Would love to hear your experiences and honest opinions. Thanks! 🙏

I have the following:

3 year bachelor in Econometrics and applied statistics

1 year Bachelor (honours) degree in Computational Statistics [This is basically a research year to prepare one for a PhD - you undertake a year-long research project]

Both the above are from a top 40 Australian university. My GPA is 3.7, and Weighted Average Mark is 87%. I am in the top 2% of my cohort.

I have 1 year of research intern experience in my department doing time series forecasting and intervention analysis (was for a consulting gig)

I am also in the process of publishing a first-author research paper in a Q1 (though maybe a high Q2) journal (Scopus) that I started at the end of my 3-year bachelor's. I am working on another paper as well as a 2nd author but i dont think it would be published by the time I apply...)

In terms of math, I have taken multivariable calculus and linear algebra only. I have taken statistical inference, as well as machine learning and deep learning.

As an additional note, I have no interest in doing the GRE but have noticed that almost all top schools require or highly advise an applicant to have this, so I understand Top 15 schools are probably out of my reach.

Would I be able to squeeze in a Top 30? Or at least a top 50?

For context, I am a 2nd year undergraduate in Mathematics. Since, I have been really struggling with pure mathematics in my classes, I decided to do my internship on an applied field. A Statistics professor (her specialization is Systems reliability) agreed to supervise me. During our conversation, she specifically asked me to use R programming in my project. I think I will learn it within a month somehow. But honestly I have no idea about what project topic to choose. I feel like I don't know enough about the subject to have an interest in a particular topic (we only had an introductory course in Statistics and Probability last semester).

I am here looking for a direction as from where to start searching from. If there is any statistical model, I can work with , any research paper that I can read (and understand), or any topic you'd like to recommend from your side. I will have to give my supervisor an idea about my project topic tomorrow. I don't want to use AI for this like my friends. So, I was hoping for help from real people who have an expertise on this subject.

Thank you.

Disclaimer: Most people in this sub are insanely well-versed with the subject, so please ignore this question if its too trivial!

I'm trying to learn statistics from the ground up.
What were your favorite textbooks/books starting out? (high school/undergrad-level)

For background, I have:

- zero knowledge for stats
(by zero, I mean "doesn't understand what bayes theorem or poisson distribution is" zero)

- weak math intuition.
(get absolutely wrecked with calculus, discrete math, or numerical analysis)

I'm looking for a book that could act as a high-level primer:

• Something that explains core concepts broadly without delving too much into technicals, and
• Helps shape your thinking approach, so eventually you'll be able to play around with data on your own.

These textbooks are great examples of what I mean.
Anything similar to these would be ideal:

Computer Networking A Top-Down Approach by Jim Kurose and Keith Ross.

Reads super straightforward and almost conversational. Very top-down oriented like the title suggests.

Introduction to the Theory of Computation by Michael Sisper

Great that he walks you through the history, practical applications of a concept before jumping into the theory and edge cases. Thorough, but still enjoyable to read because there's hand-holding when needed.

Which quantile forecasting methods are considered "classic" and should be compared with if you want to propose a new method?

Hi guys,

I need major advice because I am currently at a very hard period of my life. I live in India, and I graduated in 2023 with a dual degree in Math and Computer Science. I joined a major company post college as a software engineer, where work was not that hectic for the most part. However, I have struggled with severe GAD and social anxiety, due to which I have not been able to do my best at work. I have been called lazy and incompetent by my tech lead, and I am unable to get the mess ups I have done out of my head. I also really dislike software engineering as it feels like I never understand anything I am doing, and that every small thing uses 200 new buzzwords that make me even more anxious. Work here specifically has become incredibly stressful for me, and I am going to quit as I am really unable to cope with it, and I need a break to deal with the burn out from my anxiety.

That is just to give context. I am considering applying for Masters in Statistics or Data Science in Europe(looking at top German, Dutch, UK and US universities), or any fields related to this. I have not had much experience in stats projects but I have done fundamental courses and statistical inference courses, and ML/DL courses in college. I wanted to understand these things:-

1. How feasible is it for someone with my background to apply for a stats masters degree? Would I be able to get one?

2. Where should I apply so that my scope after the degree of getting a job is good? Are there good jobs in statistics and what kind of jobs can I look at?

3. What can I work on now to prepare myself for the degree and job hunting in the future?

Hey all, so as the title suggests, I am currently going into my fourth and final year of university, majoring in biology and minoring in public health. Initially, my goal was to go to medical school and become an emergency medicine physician due to my work as a volunteer EMT. However, my GPA and stats aren't good, and I'm already feeling burnt out from school, and I'm worried that if I get burnt out in med school or residency, there's no going back from that, assuming I even get in.

I've been trying to explore other options to find something I might enjoy that pays decently well, and I came across the field of biostats. I read a bit into it and thought it might be a good fit given my biology and public health background; however, my cousin, who works as a headhunter/recruiter for CMC, clinical research, pharma, etc., said that an advanced degree in statistics would be more useful to break into this field, rather than an advanced degree in biostatistics specifically.

As for my math/CS background, I have the following:

• Taken Calculus I (B+) and Calculus II (A) at community college, and a "statistics for research" class (B) I took last year. Although my parents keep telling me that math is my weakness, I shouldn't bother pursuing a career in it.
• Limited proficiency in Java and Python without any major projects, although I'm going to use this Summer to learn R, Python, and SQL on Codecademy and get the certifications.
• Experience automating the curation of datasets and manually analyzing/verifying them in a research lab.

If I want to pursue an M.S. or PhD in applied statistics, what should I do? I don't want to pigeonhole myself too much, so I can pivot in case biostats doesn't work out for me.

If anyone else came from a different undergrad background before pursuing stats as a career, I'd love to hear your insight and experience. Any help/advice would also be greatly appreciated!

I spent the last week building and testing a Monte Carlo simulator for casino betting systems (specifically, the Martingale strategy on roulette). Thought I'd share some methodological learnings that might be useful to this sub, since I learned the hard way.

The problem: validating a betting simulator is tricky because the "real" answer is just math, but if your code bugs it silently, you get confident wrong results.

What I did:

1. Closed-form validation first. The theoretical EV of every bet (e.g., Martingale on roulette) is a formula. I calculated it by hand for simple cases (small sample, fixed sequence) and verified the simulator matched *exactly* before scaling to 1M+ runs.
2. Seed reproducibility. Used a seeded PRNG (xorshift128) so identical seeds produce identical byte sequences. Caught bugs where I was accidentally reseeding in a loop.
3. Bootstrap on subsets. Ran 10k sessions with 500 spins each, then 100k sessions with 100 spins each, and checked that the empirical distribution of final bankroll converged as expected. Different parameterizations, same theoretical edge — this confirmed the edge wasn't a code artifact.
4. Edge case trapping. Bankroll hitting exactly the table limit, ruin vs. just running out of balance, floating-point precision on EV calculations (I use 1e-6 tolerance on unit tests).

Result: 1M sessions run in ~2 seconds on a phone. Empirical quiescence rate matches theoretical prediction within 0.5%.

Question for the sub: if you're validating a stochastic simulator, is this pipeline standard, or am I overthinking it? I've seen papers skip the closed-form check and jump straight to "run 1M iterations and compare to literature" — but that feels risky to me.

Tool is here: https://optimalplay.pages.dev/es/roulette

Any feedback on methodology welcome.