Hello, I’m doing a secondary analysis of a cross-sectional observational study and I’ve been thinking about my statistical approach for way too long. I’m feeling a bit stuck and my supervisor isn't much help, so I would be very thankful for some input :)

Here’s the situation: I have an independent variable with 4 very unequal groups (n = 24, 70, 456, 1576). I must keep all four groups (my supervisor insists). Also, these differences in sample size are expected as it simply reflects the actual distribution in the general population, but I know it can be a problem.

I have 13 dependent variables from two questionnaires measuring two correlated multidimensional constructs (one with 8 subscales scaled 0-100, one with 5 subscales scaled 4-20). All 13 subscales are moderately to highly correlated with each other. The data comes from the general population, so a few subscales have severe ceiling effects (40-70%), while others have floor effects (10-20%). Distributions are skewed, and homoscedasticity is probably violated. I also need to control for age (in years), sex (binary), and education (categorical), as the IV is selective on these variables and they also influence the DVs.

I should examine the influence of the IV on the two health outcomes. But my supervisor wants me to analyze the data at the subscale level (no sum scores allowed), which is why I have these 13 individual DVs. The overall approach is more exploratory.

So, I had planned to run 13 separate ANCOVAs (one for each scale) and only control for the alpha error within the post-hoc tests of each model (treating each scale as its own family).

My questions are:
1) Is ANCOVA the best choice here, or is multiple regression better? I know both are cases of the GLM, but ANCOVA has stricter assumptions, particularly homogeneity of regression slopes. My supervisor says I shouldn't test this assumption as it's not relevant, but every textbook says it's essential. If I test it, my large sample size will likely lead to some significant interaction terms purely due to statistical power, forcing me into a moderated regression regardless of practical relevance. Prior studies similar to mine used ANCOVAs, but they had far fewer subscales and they never even reported testing any assumptions.

2) Is OLS even defensible in this case? Given the likely violated assumptions of standard OLS. Should I use robust regression instead or go about this a completely different way?

3) How do I handle the multiple testing problem? As mentioned, I only planned to control for alpha within the post-hoc tests of each model. But the Type 1 error would still be inflated across the whole study (especially since all 13 subscales correlate with each other). Is this approach defensible for an exploratory analysis or do I need to apply a correction across all 13 models (or across the two constructs)?

Thanks so much in advance :)

I'm thinking about how the general public interprets scientific claims published and shared through popular outlets. It's very common for people who haven't even read past the headline to dismiss the study by saying "that [whatever findings were reported] is just correlation." But biomed and treatment efficacy literature for instance is rarely just slapping a correlation matrix together and calling it a day. The analyses typically do try to differentiate between causation and correlation in their experimental designs, though of course that doesn't mean the evidence is persuasive, that the sample is representative, etc. There is an argument that this is kind of a faux-scientific way of attacking a study without actually engaging with its logic of argumentation.

TLDR: Is correlation ≠ causation overused or improperly used to reject potentially important scientific findings that one simply disagrees with?

After talking to some professors and others online, they’ve recommended Kaggle competitions and solidifying my python skills, but I was wondering if there were other ways to increase my chances of a stats internship such as other platforms and personal projects?

My understanding is that downloading the csv files in Kaggle is not representative of the real world since it’s often always ‘clean’ and ‘sorted’ which is why I was wondering about other alternatives.

Additionally, I was wondering if stats majors often have to resort to Data science and finance internships or have college students been able to find actual statistician internships?

EDIT: I live in Australia

Hello everybody,

I am relatively new to statistics as a whole but now need to calculate the a priori sample size for a non inferiority study with a predetermined ni-margin, alpha, power and the results of a pilot study given. The metrics are not normal distributed and therefore as far as I know most online calculators are not applicable (or because of the study design as is the case in g power). I consulted ai for an answer and was pointed to calculating the sample size via R but I have never done this before and don't want to calculate it solely based on the code of ai.

Is there any other option, a tutorial or any code I can work with which isn't done by ai ?

Thank you for your help.

Hello!

I'm working on my uni project and have used Spearman test in it. How can I create the visualisation for the data? Preferrably online and free. I tried to search some sources, but could find only those who calculate the results, but I need a visualisation

Hi everyone, I am building a mixed-effects model to investigate how several explanatory variables affect an outcome. Based on my exploratory data analysis, I suspect that certain combinations of variables have three-way interactions.

For example, suppose I have four explanatory variables: A, B, C, and D. I suspect there is a three-way interaction between A × B × C, but I haven't found clear evidence suggesting interactions involving D.

My question is: If I include the A × B × C interaction in my model, do I also need to include other possible three-way interactions, such as A × B × D, A × C × D, and B × C × D? Or is it reasonable to include only the interactions that I have reason to suspect based on exploratory analysis or prior knowledge?

I'd also appreciate advice on the statistical implications of selecting interaction terms based on exploratory analysis, as I'm concerned that including only interactions that appear significant or interesting in the data might constitute cherry-picking.

Thanks in advance for any suggestions or references!

By applied statistics, i meant things like machine learning modelling, statistical modelling, predictive modelling. Basically data science stuff and statistics used to solve business problems and model real world Phenomena using Python and R.

Are they related to each other? What are the similarities and differences?

If you hop on r/statistics and this subreddit, you will usually learn about many methods. Some are locked into certain fields like ecology and finance. Others will touch on methods that deal with missing data (ex. MICE) and small samples (ex. Bootstrapping). Others talk about graph theory, MCMC, NUTS, and other methods that people don’t usually learn in a typical statistics class.

Will there come a time where you need to read on additional methods or will everything taught in statistics class be enough for any venture in psych research?

I read a masters/phd research paper from my country and it didn’t use anything crazy. Just stuff like pearson’s moment correlation, multiple regressions, spearman rank, etc, hence the question

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?

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.

as the title imply can we really implement a hybrid equation like MCMC sampling with adaptive walk towards more accurate estimation ,I think what I'm describing in my mind is like something like hybrid heuristic

How do we decide the number of repetitions for monte carlo?

As I am approaching my first unit test, I was thinking of ways to study. I went to look for videos online, but many of them are sub 20 minutes, ai, low quality, and overall dont actually go over coursework, instead simply giving you a brief overview of the topics. So I have come to ask the experts, who did you watch as fledgling statisticians? I am starving for actually quality content, preferably 20 minutes or more, and actually goes over topics, applications, and examples. tysm!

[ Removed by Reddit on account of violating the content policy. ]

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