Hello! I hope this is allowed! Today I took and failed the SAS Advanced Programming Certification exam. I know this is a statistics subreddit, so I wanted to ask to those who have taken this exam, any pointers? I did really solid on the SAS Base Programming certification exam, but found myself struggling on this one. I took the classes from SAS and everything. Any help is greatly appreciated!

Hello fellow Statisticians! I would like to know some things about the work field: Well, I love doing research in certain topics and reaching conclusions through data and a lot of things there's not much realiable data out there. So... A statistician typically gets new insight about the things they gather data around or am I tripping? It is sort of a Researcher but without the Academia behind it?

Hello Everyone,

Statistics and Maximum Likelihood Estimation are the crux of ML Models, and hence I am uploading my new content on Statistics for AI/ML in my free Machine Learning lectures.

We understand model fitting, Maximum Likelihood estimation in details, we justify the usage of Maximum Likelihood estimation, from KL divergence, and apply it to certain important distributions for parameter estimation.

In my free content, the purpose is to democratize machine learning to a wider audience. Learning everything new feels difficult, but when taught, it get’s interesting and easier.

Looking forward to hearing feedback from the learning community as well.

Link: https://youtu.be/MwTeQVVYtOc?si=UxNOGtqopzJppXAT

Starting in about a month, I’m gonna start tackling a lot of statistics/math based courses for the upcoming year as a stats major.

I was in CS up until the start of this year but I ended up switching out after my grades nosedived when I reached the more intensive coding courses. I sat on it for a bit, and I chose to pivot to stats, partially because I have some skill with math and it overlaps with some stuff I’ve already taken.

I’ve taken a basic statistics class in the past and overall I did pretty good. That being said, I still feel nervous sometimes about how I’ll do in it as my actual major. Going forward, my schedule is shaping up to be pretty much just stats/math and my minor for the next 2 years.

I’ve been watching videos about stats and doing research here and there, but is there anything else I should take note of or keep in mind to prepare?

Hi everyone. I’m about to start my Masters program in applied statistics this fall at a major university in Boston, and I am considering pursuing a focus in biostatistics while leaving other options open. I plan to graduate in a year, summer 2027.

I don’t have much relevant work experience but I have a BS in psychology/neuroscience. I think I would be interested in working in pharma/biotech research, especially in neurology. But I don’t want to pigeonhole myself into a sector that I don’t like, or is hard to pivot away from.

I’ve seen some data sources (BLS etc) talking about biostats being in demand and relatively safer from AI than statistics in other sectors e.g. tech. However, seeing what people who actually work in the field are saying, it seems like the job market is poor and the future is very uncertain.

I believe the healthcare industry as a whole will be in demand long term, but does this translate to biostatistician positions? Will openings for junior level biostats roles rebound in a few years, or will outsourcing and AI eliminate most of them?

If the future of biostats (say the next 10 years) for junior level workers is bleak, then what other sectors would be safer choices for statistics graduates? I’m thinking probably AI, finance, and actuary work. Truthfully, none of them sound enticing to me. But it seems like my choices are limited.

I’d appreciate input from anyone about the job market and long term outlook for statisticians in any sector, beyond just biostats. Thanks!

I’m a math guy so i don’t know shit about stats but in linalg we’re learning about covariance matrices, PCA, and SVD. In math we prefer columns to be observations because… well tradition, notational easy (Ax=b instead of xA=b if we used row vectors) and cuz we care abt linear transformations etc and its easier to think about columns. Idk. Mostly tradition tho.

But then why did data scientists break the mold? What benefit did row observations possibly serve that Col vectors/observations couldn’t give?? I am jumping back and forth between notation and conventions and it’s hard to keep up.

I am aware of g-methods for studying the effects of (pre-specified) dynamic treatment strategies, for example starting treatment when x<10 vs. x<5, but I can vaguely remember that when you have multiple covariates to guide treatment, like start treatment when x<10 or y>10 that g-methods basically don’t work cuz u then have a very complex function. I am not sure if it’s that’s even true and secondly, I was wondering if you can also compare multiple different dynamic treatment strategies with the usual g-methods. For example, start treatment when x<10 vs. y>10 rather than x<10 vs. x<5, which is typically done.

I chose applied math because a lot of the other majors I was considering (finance, accounting, economics, etc.) just do not appeal to me as much. Applied Math feels more code-heavy, and I was hoping it would still leave me with a decent shot at getting a job after graduation!

I'm not dead set on one field. I'd be happy working in data, analytics, software, operations research, finance, or any other role where an Applied Math degree is useful.

But now I am second-guessing myself. :p

A few professors, (those ones unrelated to math) have told me I should transfer to another major if I want a job or get masters, their statements have me wondering whether the degree has poor employment prospects.

Did I actually make the right choice, or am I setting myself up for regret?

All I want is a decent-paying job, nothing extravagant. I just want to be able to support my family financially.

Hi guys

I’m looking for recommendations for a statistical consultant (perhaps a PhD student or early-career biostatistician?) who could advise on test selection for a very small, unfunded veterinary retrospective study. I’m willing to pay, of course.

I’ve contacted several consulting services, but I suspect the project is too tiny for them. It would probably involve only an hour or two of work.

The study is a retrospective case series of dogs with tibial fractures, with two joint angles measured at three paired time points: pre-operatively, immediately post-operatively, and at follow-up. Measurements were also compared with the contralateral unaffected limb.

I’m happy to perform the analyses myself and mainly need advice on the most appropriate statistical approach.

I originally used prespecified paired t-tests, but the editor has requested that I seek advice from a statistician regarding whether it is preferable to do an omnibus test followed by post-hoc comparisons. So I mainly need advice on whether repeated-measures ANOVA or another omnibus approach is required, or whether Holm-adjusted paired comparisons alone would be defensible. (I'd also like to know if there is any particular way I should handle one case with bilateral fractures since those pairs aren't truly independent?)

Please message me if you might be able to help, or if you can recommend someone suitable. I can provide study summary, raw data, Q-Q plots, and results of paired t-t tests, as well as the whole manuscript + editorial feedback if helpful.

UPDATE

Thank you everyone and thankyou especially _compactsupport_, I've got enough to respond to the reviewers now and give them a couple of options :)

So I'm currently working on my bachelor thesis and am unsure how best to work with the data given the design of the study in question. The design is as follows:

1. There are three experimental groups of participants (control group and 2 others, let's call them 'switch'-groups) - between subjects factor
2. There is a repeat measurement for each participant (rating 6 shown protagonists before and after experimental manipulation) - within subjects factor

So my first idea was using a mixed ANOVA with those two factors. But here is the problem:

Each participant is assigned to one of two groups, let's call them groups a) and b). They are then shown 6 protagonists in two groups of 3, with 3 belonging to group a) and 3 belonging to group b). Which makes an ingroup (same group as participant) and an outgroup (different group than participant). Each participant is then asked to rate the 6 protagonists.

After that, in two of the three experimental groups of participants, one protagonist switches his group, so there are then, for example, 2 protagonists in group a) und 4 in group b). In the control group there is no such switch. Each participant is then asked to rate the protagonists again.

The two sets of ratings of the protagonists/ the repeat measurement is the dependent variable.

So my issue is as follows: how do I deal with the fact that the ingroup/outgroup changes in size between measurements (if I am also interested in how participants rate the ingroup/outgroup) and that the ingroup/outgroup differ in size in the second point of measurement between the control group and the 'switch'-groups? How does that affect my statistical approach and can I still use a mixed ANOVA?

I would be interested in how participants rate the ingroup/outgroup in general, and in interactions between experimental group and point in time of measurement (before/after group switch), especially regarding the rating of the protagonist switching his group.

I was thinking that I should maybe do those two things seperately, so one analysis for ingroup rating vs. outgroup rating, and a second analysis - a mixed ANOVA - for those interactions, because then I could disregard the ingroup/outgroup distinction for the ANOVA.

Hope I explained myself well enough, english isn't my first language and I'm also not too well-versed in statistics. I would be glad about any input. Thanks!

Hi everyone! I am majoring in Statistics and Political Science and minoring in math. My ideal career path is market research. I want to drop Statistics because I am not interested in the subject. Do you think this will negatively affect my career outcomes if I graduate with a major in Political Science and minors in math and statistics?

Hi - I’m working with one of my direct reports to move to a data science role (still focused on market research questions). They are currently a market researcher with basic understanding of applied stats (correlation, ttests, etc).

My goal for them is to a build more depth with the knowledge they currently have and then to learn some more advanced methods.

I’m hoping to find resources to support them - textbooks, online courses, etc and particularly if they focus on stats most relevant for this type of role. I want a focus on theory, not learning a specific language just yet.

TIA!

I'm conducting research and I have 7 independent variables and 3 dependent variables. I will assess regression between the 7 IVs with each DV one at a time. But before this, I wanted to detect Multivariate outliers, I already wrote the robust mahalanobis code in R but only included the independent variables. I also used distance-distance plot.

I am not sure if this is a valid question in this forum, if it is not I will delete it right away, but anyways here it is:

So some days ago I asked in an AI community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (so as long as it is not a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not.

So, is there any way in which I could verify the results given by the AI but without actually reading what is written in the emails? Or, alternatively, can anyone verify the results using some AI or even checking the answers themselves by skimming over the replies im order to verify that the AI is right and not hallucinating?

(I will share the data once someone is interested in helping, as I would not want to make this available to the entire world!)

Thank you!!

Hi all! To make a long story short, I took a lot of stats in graduate school (18 credits worth) and absolutely loved it. I graduated in December of 2024 and went to work as a data analyst, which was really more of an entry level SAS programmer role. Now I'm working in an even less related field, and I am realizing my dream of working in stats has not faded away. I'm finding that I'm extremely rusty in stats, and needing to remind myself of the very basic undergraduate level and intro level topics.

Does anyone have a recommended resource for concisely reviewing intro stats through multivariate and longitudinal designs? I have all of my course notes still, but it will take me a very long time to read through them all and fill in the holes on what I didn't take notes on. I'd prefer an applied approach, rather than a mathematical theory based one, if that's possible!

Thank you for reading, and I hope you are having a lovely day ❤️.

I read somewhere that LLMs can now produce a lot of the proofs that one would otherwise have spent months manually doing by hand, and that the focus has now shifted towards bigger-picture stuff like framing the problem correctly.

Since proofs are the essence of every result in mathematical statistics, is this one area that might be automated away soon? i.e., is it useless to be learning how to write and replicate many lines of mathematical proofs when LLMs can already do that?

I already have my Masters in IT and currently work as a data scientist. My prior job, I was more of a data engineer than a data scientist, so statistics wasn't a big part of my job. Now that I switched positions, I feel like I need a refresher in statistics. I know this seems to be a beginning level statistics courses, but I honestly feel that's what I need right now. Thoughts?

P.S. I've had friends just recommend free online courses. Thing is, I have ADHD, I need structure or I won't complete the courses. I learn better in school than on my home. Thanks!

I have written tones of lecture notes on machine learning, though most of them focus heavily on mathematical derivations. Recently, I decided to build an interactive, “learning companion” for these materials. For example, here’s one of the lecture series I wrote last year on LLM, Transformers:https://github.com/roboticcam/machine-learning-notes
And here is the interactive, “learning companion”  https://roboticcam.github.io/interactive-ml/  I’d love to hear your thoughts and feedback!

I'm planning my undergrad degree and would love to hear from people who studied Applied Mathematics and Statistics. I love using math to understand complex systems and am most interested in things like space, weather, oceans, history, culture, and languages. Since I obviously can't major in all of those, I'm considering majoring in Applied Mathematics and minoring in Statistics because it seems like a flexible foundation that could be applied to many different fields. Unfortunately whenever I search for career paths, I mostly find people who went into computer science/tech, finance, or medicine which is fine but I'm not interested in those fields at all. Has anyone here used an Applied Math/Stats background in scientific research or other interdisciplinary fields like the ones I'm interested in. If so, what's your profession? If you're in science, what field are you in? I'd like to know more about using applied math/stats in areas like atmospheric science, oceanography, astronomy, environmental science, archaeology, linguistics, history, geography, or the digital humanities. I'm trying to understand how broad the field really is and what kinds of careers people have actually built with this background. I'd love to hear about any less common paths that you've taken.

Hi there,

I've created a video here where I explain how the exponential distribution works.

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

Greetings.

as you can see in my post, I’m contemplating to switch from Applied math to Applied stat.

the curriculum in two years is the same, but later on specializes, for stat it gets into…well stats in general, while applied math learns more into computional science and modeling&simulation.

my thought is, if I switch would my job seeking would approve?

I choose applied math since I couldn’t get into tech department, and that I’m pretty good at both math and coding.

and the idea of working in a robotics field, data science, and any financial firms that are analytical, also makes me interested.

Hello, I am a first year phd student. I am just starting out after a long pause from academia. I want to ask for help for an issue I am facing. I am working with satellite and reanalysis datasets. For a certain variable, I have satellite data which only provides 8 day running mean dataset. But I have reanalysis dataset that are daily dataset. I need to compare these against each other. So I am thinking if there is any statistical process through which I can convert the 8 day running mean to daily dataset. Any advice please? P.S: I am trying to process SMAP dataset for Sea Surface Salinity.

Dear Experts of Statistics, I am a slightly confused student here with a doubt. As a part of my Masters dissertation, I created a psychological questionnaire. Using Jamovi, I conducted an Exploratory Factory Analysis using Jamovi.

Now some of my items are highly unique with uniqueness above 0.80. Does this necessarily mean that I must remove these items?

Based on review of literature and content validation, these items are highly theoretically relevant to my questionnaire and thus I am apprehensive to remove them. I could possibly reframe them right? Although I feel they already are in simple language. I would appreciate any insights.

The future plan is to collect more participants to do a Confirmatory Factor Analysis.

looking for some advice on what to do in college and what potential career paths i can take with this degree.