Hello.

I'm in university doing a humanities degree and I'm required to pass a introduction to statistics course. I'm worried that I lack the foundational knowledge to pass it because I believe that my ability is at about a 4/5th grade level due to lack of understanding beyond that point. Off the top of my head, I can do the following:

Addition, subtraction, multiplication, (basic) division (as in, not long division), PEDMAS (this sort of depends on the question but I am able to do it eventually), work with basic graphs such as bar graphs and dot graphs, basic geometry (can name 2D and 3D shapes, calculate angles with tools), basic algebra (when there is 1 unknown variable), tell analog time, deal with currency (change, etc), use decimals in a very basic way.

Things I struggle with (that I'm aware of):

Fractions that aren't halves or quarters.

I want to be clear this post isn't a joke. I'm asking for legitimate advice on the specifics of what I need to learn to be able to pass this course, because I have been aware my entire life that my math ability has always been not as good as others. Basically, please take this seriously and I kindly ask to not be made fun of, I'm aware this sounds ridiculous-- but it's my life. Also if you're wondering how I ended up like this: combination of passing classes when I should have been failed for most of my life and lack of intervention by schools/adults.

Any help is greatly appreciated.

Hi everyone, I'm applying to statistics master's programs this cycle, and I'm trying to gauge my chances of getting into a decent program. I'm a former math major who has several years of work experience as a data scientist, and my academic profile has some strengths as well as some weaknesses. First, do you think I'd have a realistic chance of getting into some of the most competitive statistics master's programs (such as Stanford, University of Toronto, or ETH Zurich)? If not, do you think I'd have a realistic chance of getting into other less prestigious, but well-regarded programs (such as University of Minnesota or University of Wisconsin-Madison)? Also, are there any programs that you think would be a particularly good fit for my experience/background? FYI, I'm mainly interested in theoretical statistics programs, and I'd also consider programs that have a slightly more applied focus. However, I want to avoid programs that are known as “cash cows”, or programs with material that is overly tailored to present-day data science tools and less adaptable to future changes in the data science industry. Also, I am applying to programs in the US, Canada, Europe, and maybe Australia.

In my undergrad at University of Chicago, I took the Honors Analysis (MATH 20700-20800-20900) sequence, which is regarded as one of the most difficult undergrad real analysis sequences in the country. I got an A for two quarters and an A- for the third quarter. This is a particularly strong point since statistics master’s programs really prefer candidates who have a strong background in real analysis. My professor for Honors Analysis passed away for a few years ago, but my former TA (who is now a tenure-track professor at a different university) has offered to write me a letter of recommendation. I also took the Honors Algebra sequence, and got one A grade and two A- grades.

In my undergrad, I only took the introductory two-quarter sequence for statistics majors (STAT 24100-24200), but I got an A in both quarters.

One weakness about my undergrad: I got a C in one upper-level elective math course (Intro to Commutative Algebra, which focused on the theory of modules) in my third year, due to medical issues that I was dealing with during that quarter. However, I finished with fourth year of undergrad with a strong performance, with entirely A grades in math and stats classes. My undergrad GPA in all math and stats classes is a 3.7, but it would have been a 3.85 without the one C grade during third year.

In my data science jobs, I have used a wide variety of machine learning algorithms and statistical tools, such as gradient boosting, neural networks, support vector machines, ARIMA models, and linear/logistic regression (including Lasso and elastic net). I have also used Python and SQL almost every day at work, and I am very comfortable with both languages. I have also used R occasionally, but not as much as Python and SQL.

Also, the first three months of my first data science job consisted of a training program, where all the new hires took classes and completed problem sets all day at work (much like a real mast). The classes and problem sets focused on linear algebra, statistics, and the theoretical underpinnings of machine learning algorithms. I did really well in the training program, probably the best out of all the 14 new hires. Also, my former manager (who also co-directed the training program) has offered to write me a letter of recommendation.

One thing that could potentially be viewed as either a weakness or a strength: I started the OMSA (Online Masters of Science in Analytics) at Georgia Tech, as a part-time student taking one class per semester while I worked full-time. I completed 6 of the required 10 courses, and my cumulative GPA was a 3.83. My courses in the OMSA program included probability theory, regression theory, time series analysis, and other statistics classes that I did not take during my undergrad at UChicago. However, I had to withdraw from the OMSA program before completing it, due to caregiving responsibilities for a close friend who was fleeing a domestic violence situation and was also dealing with a severe mental health crisis. (My friend is doing better now, but the OMSA program has a policy that students can only take off 1 semester in a row, which means that I unfortunately can’t return the OMSA program and complete it.) So I’m unsure if my partial competition of the OMSA program will be an advantage or a disadvantage - on one hand, I took statistics classes that I did not take in my undergrad, but on the other hand, I withdrew from the program (although I had a compelling reason to do so). Also, one of my former professors from the OMSA program has offered to write me a letter of recommendation.

Also, more recently, I think I’ve discovered two entirely new mathematical results! First, if n is a positive integer, and f(n) is the greatest integer m such that all the binomial coefficients (m choose k) are nth-power-free, I’ve found a set of lower and upper bounds on f(n). Second, for a set of n independent random variables with the Unif(0,1) distribution, I’ve found a formula for the probability that exactly k of the random variables are greater than their collective geometric mean. I haven’t published my findings, but I’m thinking of self-publishing on arXiv.

In summary, my strong academic math background and data science industry experience are my greatest advantages, but my previous withdrawal from the OMSA program is potentially a disadvantage. Also, do you have advice on things to include in my Statement of Purpose, and the best way to address my withdrawal from OMSA in my SOP? I'm thinking of briefly discussing the extenuating circumstances that came up (supporting and caregiving for my friend) and also saying that in retrospect, I've decided that I want to pursue a master's program with a more theoretical bent, as well a full-time program with in-person classes. Thanks for all your help!

I cannot find how to calculate a power analysis for a 2w x 2w x 2w ANOVA design. GPower does not support this amount of repeated measure variables and I also don’t think the R pwr package does either (please correct me if I am wrong).

Please let me know any advice you may have.
Thank you!!!

My professor is contradictory in her lessons, much of which I'm having to google to figure out what it is she said that's actually true in stats and how much of it is her not gaf.

Right now, I'm having trouble figuring out hypothesis. The questions she has us do through Pearson's AI goes differently, and Pearson isn't always correct either (Pearson is awful and I hate it)

My understanding: (I will do my best with the symbols)

Ho only uses <=, >=, =

Ha only uses <, >, |=

- The claim, according to Pearson, can be either Ho or Ha depending on the equality symbols used.

- But my professor says the null hypothesis (Ho) is ALWAYS the claim and the alternative hypothesis (Ha) is the ALWAYS the complement but applying both rules brings up a lot of contradictory answers depending on the provided data in the questions.

Example: "An Internet provider is trying to gain advertising deals and claims that the mean time the competitors' customers spend online per day is less than 45 minutes. Write the hypothesis."

The claim: u < 45 minutes

My professor's "correct" answer:

Ho: u < 45 minutes

Ha: u >= 45 minutes

- This is contradictory because Ho can't use < and Ha can't use >=.

Pearson's "correct" answer:

Ho: u >= 45 minutes

Ha: u < 45 minutes

- This contradicts my professor saying "Claims HAVE to be the Ho"

Which one is actually correct?

Personally, I think Pearson's is correct and my prof is giving blondes a bad name as usual but going back and forth on her assignments and pearson's "correct" answers is frustrating as all hell and I'm getting discombobulated. I've emailed her many times about previous assignments only to get ghosted or a response that says "Figure it out" or "Ask your adviser" (my advisor teaches biology not stats, how is he supposed to help?) so contacting her about it isn't going to get me anywhere. Unfortunately I am not available when tutoring is open either, so I can't get help there. I used to like math...

Hi. I posted here around one week ago about the US schools I should target for PhD in statistics. I was a bit vague about my qualifications. So, I am seeking advice once more giving proper information.

Nationality: Indian.

My equals: I have obtained BS-MS (integrated 5 years masters) in 2026 from one of the IISERs (reputed for basic science research in India). I have majored in mathematics and have a minor in data science.  During my undergrad, I mostly had mathematics courses and my masters thesis was also in a topic related to number theory and my minor project was in statistical data science. I want go for PhD in statistics preferably in the US and my long term goal is jobs related to finance and quantitative research.

Grade: 3.97/4 (WES conversion).

Courses I took: Machine learning, real analysis, linear algebra, group theory, mathematical statistics, numerical analysis, scientific computing, complex analysis, Galois theory, topology, ODE, PDE, probability and stochastic processes, data structures, data analysis and visualization, measure theory, commutative algebra, manifolds, differential geometry, algebraic topology, functional analysis, number theory and cryptography, Fourier analysis etc.

Projects/ Internships: All my summer internships were basically around number theory (no statistics). One of the projects were awarded by prestigious Indian Academy of Sciences. Only my minor project was in statistical data science which was done for one semester. I do not have preprint/ publication.

I have become genuinely interested in statistics through self-studying specially after I graduated and keeping in mind my long term goal, statistics suits more than pure mathematics. I believe that I have the mathematical maturity for stats but I lack formal statistics education. But I am reading Casella Berger on my own and almost finished it.

TOEFL: 95/120, planning to give 2nd try. No GRE so far.

My recommenders are from pure maths as my university does not have a dedicated stats department.

I want to know which US universities should I apply whose PhD programme in statistics department will help me fulfil my long term goal and a tier list of the Unis I can get into (top schools, moderate and safe choices with the best ROIs) realistically specially coming from maths background. I haven't mailed any professors so far.

Also, suggestions for unis in Europe/ Asia and masters programmes in mathematical finance/ applied maths with good ROI will be helpful. Any honest general advice will also be great.

Thank you.

Is there a name for the concept of one point being such an outlier that it actually shifts the average above any other known point.

For example let’s say there’s 10 people in a study, 9 of them generally average let’s say 150 lbs weight wise with normal distribution, but then one person who weighs upwards of 600 lbs and they actually shift the entire graph to the average weight now being 210 lbs.

What would this concept be called?

I'm trying to figure out how to calculate the sample size I need to ensure a certain confidence that the rank of the sample median is within a certain interval around the true median position. Digging through Wikipedia only yielded info about confidence in (real) median values, which are completely irrelevant to my application.

Background: I'm playing around with a randomized incremental QuickSort algorithm. I want to choose pivots near the median to avoid expensive "bad" partitions for long lists, but I don't want to waste time taking excessively large samples.

I'm involved in research on a demolished cemetery in Baltimore. We have been transcribing death certificates to understand the burial population. The problem is that death certificates only started to be recorded 23 years after the cemetery was established. We have 30,000 records of burials after that point. I plotted the number of burials per quarter from the records we had transcribed and in minitab, I did a trend analysis fitting different regression models, and the data best fits an exponential decay in rates of burials. The question is, how can I estimate what the curve would look like for the first 23 years for which we have no data. Is there a way to use the shape of the curve of the best fit line I have, together with a known 0 for the quarter before burials began to estimate how many burials may have taken place in the first 23 years? I feel it should be possible but I've approached the limit of my math/stats knowledge. Any advice would be greatly appreciated.

I have to pick one of the following classes:

Machine Learning

Scientific Computing with Python (what im leaning towards)

Intro to Mathematical Proofs

Stochastic Processes

SAS Programming

I have a computer science minor so it should be worth noting I have plenty of experience in programming languages. But, Python.

Hello, I’m planning on taking an introductory statistics class over the winter and I was wondering if this is a bad idea? I plan to put the work in although I don’t want anything under an A. And another thing is Khan academy’s 16 unit overview of Statistics and probability a good way to prep ahead of time?

For some context: I’m not currently employed and it’ll be the only class I’m taking over the winter.

Our lab has been using JMP for clinical data analysis and mouse data analysis, mostly to account for donor variance and batch differences between mouse experiments. JMP is pretty expensive though, so are these things we could do in R? I know stats and plotting can all be done on R in general, but I'm not exactly sure what JMP does to account for donor/batch differences to find a starting point to learn it in R.

Or is there something cheaper or free that would work? I'd like to avoid the 1400 USD/year price tag, partly because they seem so anti-open source while charging absurd amounts

I’m working with a version of Pong where, for example, a player has to hit a moving ball with a paddle, but every time they successfully hit the ball, the paddle becomes smaller, making the next hit progressively more difficult. Once they miss, the paddle resets to its original size.

Each round lasts 30 minutes, and I would like to compare performance across rounds.

I also have a chance/control condition where the paddle doesn’t move, so some hits can happen simply by chance.

I would like to get a single normalized hit-rate/performance value for each round that tells me whether the player performed well or poorly overall.

Ideally, this value would take into account:
\- The paddle getting smaller after each successful hit

\- The paddle resetting after a miss

\- The fixed 30-minute duration

\- Different numbers of hits and misses across rounds

\- The chance-level performance from the stationary-paddle condition

What would be the best way to calculate this as one normalized value? I’m particularly interested in something that accounts for the increasing difficulty as the paddle gets smaller, rather than just calculating hits / total attempts. I have tried to calculate a score using the difference between the actual number of hits and the expected value of hits, but I think it does not take into account the paddle starting from where they last hit the ball (if it did not miss).

Also, if anyone knows of similar studies or experiments that have used a comparable normalized performance/hit-rate metric, I’d really appreciate any references or examples. Thank youu

Hi, I'd like to make an analysis on Procurement Auction/ Call for Tenders, but I can't find the losing bids for every bid in my state (Italy). Can I calculate the winner'curse anyway or what others data can I calculate to make my analysis still informative? Thanks all

This is a fairly basic survey question, so hopefully it can get a quick response. Plenty of survey software programs will let you pipe in the responses from one question to use as the rows in a matrix question later on. Say the first question is “list your friends,” then the second is “For each friend check which pets they have… dog, cat, fish, etc.”

I basically want to do that, but in a case where the respondent may have dozens or hundreds of things they’d need to list off in that first question. Is there any survey software that would let you just paste in a list/table or something?

Sorry to ask, btw, when I tried to search for an answer I just got a bunch of results for pre-loading response data. If all else fails I can just have people do everything in a spreadsheet, it’d just be nice if we could use a survey.

Hi everyone,

I'm in my last semester of a Master's program in Applied Mathematics (with coursework in statistics and numerical analysis), and I'm currently applying to Statistics PhD programs.

Since my background is not in mathematics (my Bachelor's is in Computer Science), I thought that taking an introductory real analysis course would be beneficial. I don't think that the content itself is too bad, but I find it boring, and the workload has been unreal: frequent detailed readings of notes, several weekly quizzes, and weekly problem sets. Coupled with other courses that I'm taking this semester (Mathematical Statistics 2, Linear Models, and a probability elective), I have not really had the time or energy to invest in order to do well in the course (I have a midterm in a few days and I think that I'll genuinely fail). As such, I'm considering dropping the course and focusing on my other classes. However, I know that real analysis is a key course in Statistics PhD programs, and I'm unsure if having a W in analysis on a transcript would harm my chances of admission. At the same time, even if I do pass, I am pretty sure that I will make a C at best.

Is it worth dropping the course and having a stain on my transcript, or should I tough it out and just try to pass? Another thing to note is that I have a midterm for another course (Linear Models) a few days after my Analysis exam, and part of me thinks that my time would be better spent studying for it rather than memorizing 20+ proofs for my Analysis exam.

Around June to August 2027 I will be graduating with a PhD in Statistics from one of the top 10 programs in the USA.

For quite a few years I was convinced I wanted to pursue a career in academia. Now that I have started my 5th year in the PhD program, I am having a somewhat late realization that academia is not for me.

During my PhD, I have been completely out of tune with what's going on in industry and what skillsets are valuable. I never did an industry internship during my PhD, because I was so focused on academia. I feel like I have no idea what I am doing when it comes to applying to these types of jobs, so I wanted to ask a few questions about this.

I wanted to get a pulse on what's going on in industry right now in 2026, for someone like me who is nearing graduation with a PhD. I also jumped straight from a bachelor's degree into a PhD, so I have no real work experience, which I am now noticing has made me feel very out-of-touch with non academic pathways.

1. What types of industries hire statistics PhDs? I am broadly aware of tech, biotech, finance, etc. What type of work do statistics PhDs do in roles in these areas?
2. Are remote jobs still a thing, or is that a bygone thing from the covid era? I have a strong preference to stay in my location (a major west coast city), so am either restricted to that location, or needing to find remote work, which I might even prefer.
3. Are there any skills I should be developing in the meantime before applying? I have a strong proficiency in R and Python for research purposes, but zero experience with "deploying models," or things of that nature.
4. When do jobs get posted for someone looking to start around September 2027?
5. Anything else you can think of that will be helpful for someone making a sharp pivot away from academia and feels like they don't know what they're doing now!

Even if not in response to one of the above points, would love to hear about what any PhDs in here did post-graduation. Anything to ease my anxiety over this whole process.

Hi! We are doing a research paper for a veterinary med subject where we will test an effect of a certain GAG on a chicken embryo. Question is, what would be the best sample size to use? I can only think of using G Power, but from there how do I do it? Sorry I went here because I don't really know what to do, I just got back from uni after doing a gap year.

As an honours year (Australia) student I am doing a methodological statistics research topic relating to multivariate time series under structural breaks.

My literature review involves reading, analyzing, and synthesizing the literature regarding all things time series under structural breaks. The issue is that a lot of the papers use methods that I haven't formally learned, so I am reading these papers just barely able to scrape by in terms of understanding.

Then I have to synthesize all of these somewhat-understandable papers and discuss my potential contribution with my topic. How can I do this when the literature is full of methods and lingo that I have little understanding of?

Thankfully I have a full year to execute this project so plenty of time to understand everything fully, but how do I even get started?

I have been working on causal inference and experimentation frameworks within the e-commerce tech industry lately, specifically dealing with platform behavioral and transaction telemetry data to determine how customers behaves on those e-commerce platforms.

Lately i have been facing problems where standard textbook statistical theory fails when dealing with messy, real-world data distributions as human behaviour is unpredictable and many unexpected things, which i didnt even thought could happen, can happen in the real world. Such as product delivery delay, causing the customers to be angry and hence put them off from buying the product, each of these variables have to be accounted for.

To model and have these unexpected psychological state among customers accounted for, we use variables such as sudden spikes in customer support chat volume, rapid page refreshes, or immediate session drops.

Also, Marketing headers and automated CRM systems love to push blanket promotional campaigns, but as statisticians, we know that measuring simple unadjusted A/B lift is a massive selection-bias trap.

I been trying to come up with a Statistical model that is trying to model complex human behavior—specifically isolating true customer revenue incrementality from dead-weight margin cannibalization. Essentially, trying to answer: Did the coupon cause the revenue spark, or did we just hand a discount to a power-buyer who was already determined to buy anyway even without the vouchers?

Are people in the tech industry genuinely valuing such skills nowadays? Using statistics to model and predict human behaviour and intent, answering questions like does our vouchers and ads really work?

I would love to hear your experiences how do u guys deals with the unpredictable messiness of human intent.

I’m considering taking a statistics course, and I’ve been thinking about the next steps, whether it’s significantly more beneficial for my future career to pursue a technical specialization, a master’s, or even a PhD

I am performing an ordinal regression analysis with 10 predictors, reduced from an original set of 50. Sample size: N = 380. One of these predictors is the primary predictor of interest (Safety Hazards [SH], a continuous variable). The rest are potential confounders of the primary predictor or contributors to the model.

My primary outcome is Job Satisfaction (JS).

I distilled the 10 candidates from the original 50 by running ordinal bivariate regressions on each candidate.

a) A crude model [JS <-- (beta)*SH]

b) 49 adjusted models: [JS <-- (beta)*SH + any of the 49 predictors].

1. If any of the bivariate regressions produced a CIE of 10% or more for the primary predictor, SH, it made the list of 10, regardless of its p- value.
2. If any of the 50 candidates had a p- value of < = 0.1, regardless of its CIE value, it made it to the list of 10 as well.

I sort the list of 10 by CIE value and use that order to determine which variable to test next in the model.

I then begin the final fitting process to extract the most plausible confounders of SH and true model contributors from the 10 predictors. I use 2 different regression methods: Forward Stepwise Regression (FSR) and Backward Stepwise Regression (BSR).

The conditions to stay in the model are: "If the p-value > 0.1 and CIE <10%, then reject from the model; otherwise, retain."

In FSR, I start with a sparse (crude) model, sequentially add a predictor, and test whether it meets the retention criteria. If so, I retain it, add the next predictor to the new model, and apply the same retention/rejection criteria until all predictors have been tested.

In BSR, I start with a full model and sequentially remove predictors, one at a time, and perform similar rejection/retention tests as in FSR until all predictors have been tested.

However, I find that the FSR and BSR algorithms retain and reject different variables. To be clear, about 60% of variables are common to both algorithms.

My question is: why do the FSR and BSR algorithms produce different results?

What is different about starting with a full model (BSR) versus a sparse model (FSR)?

What is different about starting with a sparse model (FSR) versus a full model (BSR)?

Please try to give an explanation that does not involve the term 'variance', as I tried to explain to my PI, only to be told that is the wrong way to explain it.

Thanks

David

Hi all, I picked up an interest in stats after undergrad & got to take it further in grad school. I took two levels of social science stats, advanced analytics & TA’d for undergrad stats.

I haven’t ruled out getting an MS in stats but I guess I still have some imposter syndrome about my fluency & whether I could succeed coming from social sciences. I’m in no rush to go back to grad school, so in the meantime I’d like to shore up my fundamentals & do some self-teaching. My question to this community is: what topics are must-know to be successful in a future Stats grad program & if you have any good free references to pass along (books/youtube playlists) I’d be very appreciative. Currently reading Bayesian Statistics: The Fun Way lol. Thank you!

Hey, I have a decent background for distribution theory and statistical inference. I wanna get into random processes. I have no idea about the resources to start with. Can anyone suggest me some ?
Moreover, are there any discussion groups and channels on telegram ir any other platform specifically for this?