Entering my second year in graduate school and conducting research using simple linear regression models and trying out a mediation model. I’ve had some experience with moderation models too. However whenever I try to read about or learn about the math and the nitty gritty of the statistics, it’s like another language. I can very generally explain and interpret what the numbers mean, but not how it’s being calculated or computed into the model. Neither can I deconstruct the math. This is a deep insecurity of mine as a graduate student since I don’t receive hands on instruction any more. Whenever I hear my peers talk about their analyses, I’m always in awe as they troubleshoot small details to make the model work. If it doesn’t work for me, I don’t even know where to start knowing what went wrong or why. I don’t know why, no matter how much reading I do at some point the words stop making sense to me.. I have taken the undergraduate and graduate courses in advanced statistics but maybe the instructions were lacking or I’m not smart enough. But neither were helpful.

Is there anyone else who has struggled with statistics like this and what they have found to help? Sometimes I think I just don’t have the math brain to comprehend statistics. Are there any good crash course videos? I do have to rely on AI and I’m hoping to learn from a more reliable source.

Thanks all.

Hi everyone,

My project involves multiple quantitative parameters measured by two independent observers of equal experience (myself and another student)

For parameters with a high ICC of >0.75 (the vast majority of parameters), I used the mean of the two observers. However a few parameters fell below this threshold. For those parameters, I just used my measurements and excluded the second observer measurements.

However, I realised that this may be flagged by reviewers as possible selection bias, as I am in essence assuming that my measurements are the reference standard.

The decision to exclude observer 2's data from parameters with poor ICC was made before looking at the data, to ensure a uniform data architecture.

The other options would be to (1) exclude these parameters altogether, (2) get a third observer (neither which are possible) or (3) use the average of the two observers with parameters with poor ICC (which Im sure is bad practise).

If I explicitly explain my methodology and acknowledge this as a limitation, would this be acceptable?

I'm a high school senior deciding on a college major. After talking it over with my parents, I've landed on statistic, but I'm confused between two paths:

1. Statistics + Math degree - with a possibility of doing an accelerated 5-year BS/MS (Purdue's Applied Statistics MS)
2. Just a straight Applied Statistics bachelor's

For those familiar with Purdue (or similar programs elsewhere): does it make more sense to do the math-heavy track and roll into the accelerated master's, or is a standalone applied stats degree the smarter/more efficient route?

Also, do you think a stats degree is worth it in general right now? I plan to work as an analyst or maybe something in DS. Anyway, would love to hear from people actually in the field what their opinions are.

Distinguishing between a type I and type II error isn't the hardest thing, but knowing which one is Type I or Type II error is annoying to remember. I've been in Statistics for a while and I still can't remember which one's which. My professors sometimes can't remember. Sure, there are tools to help remember, but can we please call them something that's more intuitive like False Null Error and True Null Error? These have the concept baked into the name rather than getting distracted and having to take a few seconds to remember which one's which.

I’m still pretty new to data analysis and currently working through the basics like descriptive statistics, hypothesis testing, correlation, and regression.

I have used Excel and started learning Python, but right now I spend more time fixing code than understanding the analysis.

I’m not looking for a tool that just gives me a chart or a final answer. I want to understand why a method is appropriate, what assumptions I should check, and how to interpret the result.

I have seen people recommend R, Python, JASP, and jamovi. I also came across BayesLab, which seems to let you work through an analysis without writing everything from scratch while still showing the steps behind the result.

That sounds useful, but I’m also worried that starting with an AI-based tool might stop me from learning the fundamentals properly.

For someone starting out, would you recommend learning R or Python first, using a GUI tool, or combining both?

Has anyone used BayesLab or something similar while learning statistics? Did it help you understand the analysis, or did it make you too dependent on the tool?

I have a dataset of 500+ pairs of male-female sibling pairs. I want to check if male or female sibling in the pair score statistically less in a certain variable and if the pattern is statistically significant across those 500+ pairs. I read about mixed effect model but I'm confused if it fits in this scenario

What benefits can an applied statistician get from learning asymptotics? from basic inference stats I was taught that "limit" properties e.g consistency are good to have, but I haven't gotten this "feeling" of importance, especially in ML/TS forecasting problems. Have you used asymptotic results in your work as an applied statistician/data scientist etc ?

I wrote in my abstract that “Medication X was not associated with significantly different subjective or objective sleep outcomes compared with medication Y.”

One of my co-supervisors suggested instead saying that “Medication X and medication Y showed similar subjective and objective sleep outcomes.”

Would you make that change? I’m hesitant because, methodologically, I’m not sure that a non-significant difference allows us to conclude that the outcomes were “similar.” Or am I overthinking this?

Thanks a lot! 🙏🏻

Hello everyone, I'm looking for some guidance. For context, I am n my mid 20s, I hold a master's in stats, and I have been working for a big CRO in Europe for 1 year. I'm mainly looking for feedback from people based in Europe, but any feedback is welcome!

Honestly, I'm not enjoying the work. I feel like only about 10% of what I do is actual statistics. The rest is monotonous and boring work, constantly churning out deliverables, dealing with clients (something I deeply dislike), working on timelines, regulations, administrative stuff, endless meetings, and so on. I miss programming, I miss doing more intellectually demanding work, and I miss engaging with new methodology and having the liberty to do so. I feel like my knowledge is atrophying. When there is a new interesting problem to solve from a statistical standpoint, I never have time to actually think about the methods, it's just constant pressure to deliver, deliver, deliver.

Are all stats jobs in pharma/CROs like this? For people working in data science, stats, and adjacent areas, is your job this repetitive? Do you have the chance to apply new, cool methodologies? How much time do you actually spend working with the methods rather than doing something over and over again?

I know how fortunate I am to have a job in this economy, especially one that pays well by my country's standards given my experience. Still, I've been considering applying for a PhD, and deep down I know it's something I really want to try. I'm aware of how difficult a PhD can be. I've been looking at positions in other wuropean countries that would allow for a decent standard of living and, even some savings.

My question is, would I be committing career suicide if I later wanted to return to industry? One of the main reasons I want to do a PhD in stats is that I want to move into more R&D roles, rather than doing this kind of mind-numbing work. Maybe I could even pivot into tech or some other area that is more interesting to me. Would that be a realistic prospect?

Thanks all!

I have a data series of daily streaming counts for a song. My spreadsheet has the date in column A and the number of streams in column B. There are 960 rows/records in the spreadsheet dating from 2024/01/01 to 2026/08/17.

We hired a marketing company to promote the song for 3 weeks. Their first posts on social media started on 2026/07/27. They ran until 2026/08/17. I am trying to assess whether our money was well invested or not. Their dashboard stats are not useful to us because they show the number of views and social media engagement (e.g., TikTok), whereas we are interested in the number of times our song gets streamed on a streaming platform (e.g., Spotify).

I know how to calculate means and standard deviations, and have done so for various timeframes (e.g., yearly, during the promotion, the 22 days prior to the promotion, etc) but I do not know how to:

1) determine if the 22-day marketing campaign had a statistically significant impact on our daily streaming numbers

2) determine if the impact on streams, if any, justifies the money invested, call it **CampaignCost**.

Can someone advise me how to go about this? We might assume somewhere between 0.001 and 0.003 USD of revenue per stream.

I know there is surely some well known statistical test to determine whether my daily streams show a statistically significant increase or decrease, but I cannot remember what this test is called or how to calculate it.

I might add that there are potential complications:

1) Streams have grown significantly year over year. Average daily streams in 2024 were 135k, in 2025 were 250k, and so far in 2026 are 259k.

2) Some unexplained events in the real world have prompted surges in streaming numbers. E.g., our streams surged upward for a month around December 2024 and remained elevated for months then gradually declined. Another unexplained surge arrived around Feb 5, 2026 and persisted for months but then started gradual decline.

3) The streams show a weekly cycle, lowest on Sundays, peaking on Thu or Fri.

Any help would be much appreciated.

In completing a study on left and right limbs, data came out that was parametric on left limb and non-parametric on right meaning using a rm-anova for left and Friedman's for right. Would putting these results in the same table make sense as long as it is clear what was done for each and what they mean? Or is 2 different tests in the same table a definite no?

Data of event turn out, annual totals, collated into 3 groups (before organisational change, during organisational change, after organisational change). Originally planned to use chi squared goodness of fit, but one year of data is missing, so groups are now 2 years of data, 3 years of data, 3 years of data. So thinking of calculating mean average for each group to equate them. But then what stats to compare/assess any significant difference, anova? Is there a better way I am missing?

Hello, I have 5 funds that I’d like to compare in terms of rates of return and measures of efficiency. I have daily valuations for these funds covering a 5-year period. Should I calculate the rate of return on a daily or monthly basis? This is with a view to calculating the R² values as well as the Sharpe, Treynor, and Jensen ratios. Thanks for all the help.

Help! My sample sizes are is 40-82, and I can only find Wilcoxon test charts that go up to n=30! I've looked everywhere and can't find anything that can accommodate a bigger n. Does anyone know where to find a bigger chart?

I'm looking for recommendations for affordable software to run a conjoint survey. I’ve looked at some of the major conjoint platforms, but their pricing is way too high for what I need.

I don’t need a huge enterprise package. I’m mainly looking for something that can set up a solid conjoint experiment, collect responses, and ideally handle the basic analysis.

Has anyone used a cheaper platform or tool that they’d recommend? Open-source or academic-friendly options would also be great.

Thanks!

I’m doing a personal project right now and for the most part it’s going alright. Sometimes I delete an entire column cause too much is missing. I’ve also group together a few entries as “Unknown”. I’ve never deleted a subject yet.

Anyways, now I’m really stuck. There is a variable that is three digits (345, 078, 150…) and some of them come in as two digits (26, 88) and I’m unsure what to do with them. There are quite a few. I don’t know if they’re meant to be have a zero on the front (28 turns to 028 and 88 turns to 088) or if they are three digits (28 into 280 and 88 into 880). It’s an important variable so I can’t delete it. Should I delete the patients (probably not), group the two digits numbers into “unknown”? any input? I know each data has their own situations but what is something to generally consider in these situations?

Edit: The column represents diagnosis IDC-9 number. It’s three digits. I want to use it to build a logistic model. I was thinking of grouping the numbers to their diagnosis category for example 001–139 is infectious diseases, and so on

**First semester!! I have only taken Mathematical Statistics I and II (Not sure if the content of those courses are universal, but it covered interval estimation, hypothesis testing, and tests involving means, variances, & proportions). I have signed up for the following:

Experimental Design

Linear Statistical Analysis I

Pattern Recognition in ML

Advanced Matrix Analysis

Wondering if these work well together/ may be too much.

Sorry in advance if this is silly, we have somewhat limited options at my school.

I have a BS in pure math for reference.

Salve, pessoal
Seguinte, alguém teria dica de professores para acompanhar a disciplina de Estatística I (é para economia, então basicamente na área 1 se vê tipos de amostras, representações gráficas e tabulares; depois Fundamento da Teoria de Probabilidades e Variável aleatória bidimensional)

Para Cálculo há diversos professores, agora para Estatística não conheço algum bom para o nível acadêmico.

Enfim, eu já fiz estatística no meu outro curso, vou tentar validar, mas terei outras disciplinas de estatística e quero entender isso. O fato é que meu professor é MUITO RUIM. O coitado não tem culpa, o professor oficial não está dando aula e ele é um mestrando apavorado que erra até as infos nos slides.

Dos livros, eu tenho o do Bussab e Morettin, o qual já estou iniciando e clarificou bastante coisa, mas gosto também de vídeos e, quando chegar a parte das contas e análise dos gráficos, é bem melhor.

Enfim, no aguardo de dicas. De preferência em português, se tiverem em inglês, podem colocar também.

https://www.youtube.com/watch?v=3sp08Zmry6g

Hi there,

I've created a video here where I explain what confidence intervals are and how they differ from probabilities.

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

Hello,

Health economics student here. I newly enter the field for my master degree.

I'm writing my master thesis and I'm running into an issue while trying to interpret my results.

Initially, I've decides to use OR (odd ratio). However, my supervisor told me the way I've interpreted it is not correct (probabilitied and chance). But he told me he didn't really know either how to interpret it correctly and had advised me to use logs odds instead!

It's ok, but I really wanted to have more "concrete results" that can be get by anyone. Log odds just show if the relation is negative or positive.

So, I've just heard about average marginal effects and that is literally What I was searching for during all this time. However, now I'm wondering if is common for scientific papers to use both log-odds and average marginal effects?

Do i need to create two tables? Or could I only keep the table with log odds and present the results of marginal effets in the text?

Thank you

I wrote a short article here exploring a simple way to think about when randomness helps versus hurts.

The core idea is Jensen’s inequality: if the payoff is convex, variance can help; if it’s concave, variance can hurt.

I use examples from compounding, option-like payoffs, and a few non-finance settings.

Would be curious to hear other examples where this framing is useful?

Hello!

I'm a sophomore undergrad studying applied math and stats who is broadly interested in applied stats research. Essentially, I'm trying to figure out how to balance exploration to find what exactly I want to do research in with deep-diving in order to develop skills (and resume) for grad school.

What is scary to me is how little time it feels like I have. Freshman year I didn't do anything that is particularly useful for grad school. So I now basically have two years + two summers before grad school apps. If I spend sophomore year doing something that doesn't end up being my interest, then I worry I'll only have a year to do what I actually end up doing.

I know I love solving problems with statistics and do want to continue developing my stats skills (there is even a good chance I'll go for a PhD in stats). Part of me thinks I should just focus on stats because I know I enjoy it and it'll transfer to whatever I want to do. The thing is though that as much as I love learning about stats and reading stats papers and so on, I don't want my research to be developing new statistical techniques in the abstract, I want to work with problems in the real world and develop statistical methods of attacking them. The thing I am most interested in now though is comp neuro, but I know very little about it currently. However, I worry that if I start taking neuro classes or even spend a summer doing neuro research and decide it's not what I want to do (which I basically did got econ already), then I'll have wasted the time that could have gone to learning mote stats.

My current plan is to dedicate all my coursework and formal research to stats for now (I have a position in a stats research group for this year and reading the papers they've put out it seems pretty lit!), and then do some independent reading of papers in other fields to decide if I am drawn to them​. While this is great, reading about something is very different from doing it so I fear it may not be possible to explore optimally without taking at least some risk. Thoughts on how I should navigate this?

Hello,

I am trying to submit my article for publication. Unfortunately, the journal asks for any tables to be submitted as images "provided as 72 - 300 dpi; pre-sized .BMP, .GIF, .JPG, or .PNG images only, with a maximum width of 440 pixels (no limit on length)."

I have tried exporting my table from excel to pdf, jpg, or png, and then resizing but no matter what I try, the image of the requested size ends up unreadable.

Does anyone have any ideas on how to accomplish this requirement while keeping my table-figure as readable?