If I want to estimate what fraction of roulette players betting on red are still ahead after 1,000 spins, is the normal approximation good enough, or does the discreteness matter enough that I should just simulate? Any pitfalls when comparing American (18/38) and European (18/37) wheels?

I'm actually really interested in statistics and related fields. I'd love to learn about the different branches of this field and some major international companies that use statistics, so I can research how they apply it in practice.

I'm new to this field and just taking my first steps, so please don't judge me for my question, even if it seems stupid

Hello. I need help with a practical problem.

There is an unknown total number N of identical items. I hold one item. Once a day, my current item is exchanged for another item chosen at random from the remaining ones (assume uniformly among the other N-1 items). Every day I mark any item I receive with exactly the same mark — I cannot tell individual items apart, only whether the item has been marked before. I keep a log: for each day, whether the item I received was already marked or not.

So my data is a table: day number, and marked/unmarked.

I need a dependence of the form

f1(parameters estimated from the data) = f2(N, probability of N)

i.e. a way to compute, from the observed table, a probability for each possible total number of items N.

As the title says, I could really use some help understanding multivariate fractional logit regressions.

For context, I am working on a project proposal that examines how individuals distribute a fixed number of units/shares across several programs. We originally planned to use a Dirichlet Regression until we realized there is an overabundance of zeros (e.g., people allocating all their shares to one or two programs and none to the others). As an alternative, a multivariate fractional logit regression was proposed, and I am admittedly struggling to understand what this change will mean for the analysis. If someone could explain it in the simplest terms possible, I would greatly appreciate it. If any additional context/information would be helpful, please let me know.

Thank you in advance for any assistance you can offer!

Any advice? I also have credit for stats 100 should I take stats 107 or 200 as someone with no prior coding knowledge?

Hi all, I'm trying to figure out what environmental variables drive the distribution of different shark species, my data consists of a column for species (catshark, blue shark etc) and then columns of the variables (temperature, depth, chlorophyll etc, all numeric data). Can I run a GAM model with species as a response variable and the environmental data as predictors, or should I look into something more along the lines of Bayesian/probabilities? (I'm working in R). Would really appreciate some advice.

Thanks all!

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

For an even-money bet with a house edge somewhere between 1% and 5%, the expected loss grows linearly with the number of bets while the spread only grows with the square root. Is there a simple rule of thumb for when the expected loss outweighs the typical swing, say two standard deviations?

Hi everyone, I’m relatively new to publishing research papers, and I wanted to get some thoughts on a figure-formatting practice I’ve been using.
When creating plots with multiple subplots, I sometimes label the y-axis as “Value” rather than repeating the full metric name and its units, especially when these are already clearly stated in each subplot’s title. So for example, my plot would have “Height (cm)” in the title, which is already what the y-label would have normally, and I would label the y-label as “Value”.

My reasoning is that it reduces redundancy and keeps the figures cleaner. Some metrics also have rather long names that can be difficult to fit along the y-axis, and using shorter labels helps save horizontal space when arranging multiple subplots on a single page. In some cases, I’ll label the y-axis as “Value” only for the first subplot and leave the y-axis labels blank for the remaining ones.

I’m wondering whether this is considered acceptable practice in academic publications, or whether it’s generally better to use the full metric name and units on every subplot’s y-axis for clarity and consistency.
I’d appreciate hearing how other researchers, especially reviewers, would feel about this approach, thank you!

I'm preparing for the GMAT and came across this Data Sufficiency question (attached).

The supposedly correct answer is C, but I would argue that E is also possible, depending on how percentiles are defined.

Here's my reasoning:

If we combine both statements, couldn't we have the following scenario?

• Section A (50 students): All students score 7, meaning the 90th-percentile score is still 7.
• Section B (100 students): The first 29 students score 7, and the remaining 71 score 8, meaning the 30th-percentile score is 8.

This would satisfy statement (2), since no student in Section B scored lower than any student in Section A.

However, when combining both sections, the 75th and 76th students would both have scores of 7, giving us a median of 7.

Alternatively, if all students in Section B scored 8, the combined median would be 8, while both statements would still hold.

Therefore, wouldn't the answer be E (both statements together are insufficient)?

I understand that different percentile definitions handle tied values differently, but is there a specific convention the GMAT expects us to use that rules out my example?

Am I misunderstanding something about percentile rankings, or is the wording genuinely ambiguous?

Hi all! I'm working on analyzing a dataset that contains risk scores for students. A student can be assessed as "High", "Moderate", or "Low" risk on this assessment according to an evaluator. Furthermore, a student can be evaluated multiple times (in practice, the distribution is log-normally distributed, with most students being evaluated between 1 and 9 times, though the maximum seems to be up to around 32 times).

The question I'm trying to answer is: Is there a significant level of variation in a student's risk score? Some related questions I have are "do students risk score fluctuate significantly the more they take the test?", and "at what point does this fluctuation/variation become significant?"

I've done some exploratory analysis plotting the proportion of H, M, and L scores over time, and it seems like (descriptively speaking) the more they're given the assessment the more likely they are to be assessed as High risk.

Based on what I've found searching the internet, it seems like ordinal regression is the best way to go about this, but I wanted to get some more opinions. I'm a bit nervous because I've never done ordinal regression; currently studying this series of videos from the National Centre for Research Methods (https://www.youtube.com/watch?v=rPcMcW25PPA&t=1s).

P.S. I'm trying to learn more about the world of applied stats, specifically, what kind of tests are out there; my undergrad was in the applied social sciences so I have some level of familiarity with basic inferential statistical tests, but I've worked as an analyst for a couple of years and I want to keep growing! I have no idea where to start or how to go about this journey so if anyone has any resources they could share I would greatly appreciate it.

Thanks in advance for the help everyone! And please let me know if you've got any questions.

Hi,

I am conducting a bias analysis for a clinical model where we consider multiple statistics, one of them being AUC.

We would like to check if the model performs significantly differently on a predefined sub-group of patients. (Think one BMI category vs the rest for instance).

For the other metrics we are using a permutation test, to check if each sub-group is significantly different than the others

However, i am unsure if we can do the same for AUC, or if there exists a better method?

I'm a pretty amateur statistician (that is to say, I'm not a statistician), but I had a question:

Let's say I've got a sample of data with some mean and sample std dev, and I believe that my population has some population mean and std dev and I want to test to see if that's reasonable.

Seeing as I don't know the pop std dev, I can use a t-test to test the mean, but how would I test for the standard deviation?

Sorry if this is unclear or commonly asked!! Thanks heaps

Hello,

I just completed a master's thesis in pharmacoepidemiology and have been offered to write papers based on my work. I am hoping to start a PhD in the near future if possible.

I have no formal education in statistics other than the mandatory parts in the earlier courses during my degree. I generally have a good statistical understanding and a sense for maths, but I want to be more confident in my statistical knowledge. That is especially as I will hopefully be working on simulation studies among other projects, and I really want to make sure that I know what I am doing (and writing).

What are some essential books for medical statistics and epidemiology that you would recommend? Are there also any practical "handbooks" that I can also use to provide a brief overview of what I should be expected to know at this level?

Amongst others, I have referred to the following books so far:

Modern epidemiology by Lash

Essential Medical Statistics by Kirkwood

Statistical Models in Epidemiology by Clayton and Hills

I am of course also doing my part and constantly reading up on statistical concepts I feel less confident in or am unfamiliar with, but any tips would be appreciated! I know I can't know everything, but I always feel bad if the quality of my work is lessened due to my own (unintended) ignorance and non-statistics educational background.

I’m considering majoring in Statistics and would love to hear about people’s experiences with the degree and where it has taken them career-wise.

What careers did you end up pursuing, and what kinds of jobs or starting salaries were you able to get?

If you went into finance, data science, or another field, why did you choose Statistics over a more specialized degree in that area?

Do you feel your Statistics degree gave you a leg up in the job market because of the quantitative skills you developed?

Did you have to spend a lot of time outside of class learning technical skills like Python, R, SQL, or other programming tools that weren’t covered enough in your undergraduate curriculum?

How important do you think networking and coffee chats are for Statistics majors when trying to land internships and jobs? Is networking just as important as it is for business majors, or can strong technical skills and projects carry more weight?

Have you ever regretted not majoring in something more directly related to your career, like Finance or Data Science?

Looking back, would you choose Statistics again, or would you do something differently?

I’m trying to understand whether Statistics provides a meaningful advantage through its quantitative foundation and flexibility, or whether a more specialized degree would have been more beneficial. I’d really appreciate hearing both the positives and downsides from people who’ve experienced it firsthand!

I want to figure out my "hottest takes" on movies using Letterboxd ratings, which range from 0.5 to 5 in 0.5 increments. My initial idea was a z-score, where a larger absolute z-score for my rating relative to a movie's rating distribution indicates a “hotter” take. However, many movies' distributions are non-normal (highly rated films are often heavily left-skewed) and bounded (no ratings below 0.5 or above 5). Does anyone have a good metric in mind comparable across movies? I considered some normalized median deviation, but I'm unsure how to make it comparable across movies with different distributions.

For christmas. Hopefully something that can further proliferate the love for statistics

I am analysing survey data. There are 6 questions that were answered with either Yes / No / Unsure.

All survey respondents (400+) completed all 6 questions.

I'm analysing each question separately for difference in responses (proportion of Yes / No / Unsure) between two separate groups of professionals. I am not comparing the results of each question against each other.

Do I need to adjust the results for multiple tests/comparisons?

Estoy en una maestría de administración, llevo una materia de estadística aplicada a la administración.
Anteriormente ya había visto estos temas en mi licenciatura por lo cual se me facilitó un poco, pero en el tema de prueba de hipótesis tuve discrepancias con el doctor.
Me cuestionó por poner el símbolo “<“ y “>” en mis hipótesis nulas, eran 5 decisiones, él dice que eso es lo más innecesario en estadística, únicamente el símbolo de igual debo usar y ya, a lo cual me generó dudas porque en la licenciatura siempre me decían que era necesario poner “<=“ o “>=“ en la hipótesis nula.
Alguien que pueda ayudarme con mi duda

However, you’re interested in the statistical methods that power them?

You’re not into deriving data patterns and predicting per se nor are you interested in any domain, but you like figuring out how people do these things correctly. You like understanding how a result is accurate and how much skepticism and critical thinking go into choosing the right method and figuring out if the result is lying to you. You like reading about the dilemmas on choosing what method to use being posted on this subreddit, however you hate finding patterns and predicting because you’re not into uncovering new information and predicting something. You just like the journey that gets you there.

You also like reading research methodologies found in psychology research papers and love hearing how every topic discussed in psychology is backed by empirical evidence, despite not being into psychology, which drove you to statistics in the first place. You wanted to study statistics because it helps us verify if something should be believed or not. You want to know what every statistical method is and why they do their thing properly, not find out new things about a domain of knowledge.

Statistics projects like making AI, doing machine learning, etc feel horrible to do since you don’t wanna build a product

I want to rank several countries' characteristics (like economic power, military power, population...etc) in a logarithmic scale.

However, there are some countries that do not have any army (like Andorra) and in that category (and related ones) they have a value of 0. However the log(0) does not exist. So, how could I convert the values into something that could be used to make the logarithmic scale?

I have thought about two methods:

A) To every value in each category, I added the values of the "smallest" or "weakest" country (that does not have a 0 in any category, so that all categories have a non-zero number). In this case, I added the values from Seychelles to all countries.

B) To every value in each category, I added "1", so that in every category that the countries without armies have a "0", the minimal number is "1" instead.

I feel that the first method has the advantage of adding a proportionate quantity in each category, since you will see that for some categories all values are small (like 1,07; 2,08; 3,1...) and in other categories the numbers are much bigger (5, 20, 49, 80...). So in method A the added numbers are proportional to the values of each category

However, it feels arbitrary to add different numbers for each category instead of adding the same for all of them (i.e. 1). However, in this case, adding 1 to the small-values-categories seems to significantly modify these values, while in the case of categories with bigger numbers, the resulting number is almost the same as the original one (like, adding 1 to "1,1 "would increase it by almost the double: "2,1"... however for other categories with a number like "50", after adding the "baseline value", the difference is negligible: "51").

So which method do you think is more accurate?

These are the values: https://docs.google.com/spreadsheets/d/19zjwdoLum0yhIQ2v3m19kz5_IplwSJYZX6e0wMqMHoQ/edit?usp=sharing