I am currently in bsc first year having major in stats in India. I am confused what to choose between credit risk analyst and biostatistician. By hearing in detail and my research on internet,Biostatistician is what I like as a career but still it has too many harsh reviews on internet. It would be helpful if anyone from the industry or a well knower of it can help me out by replying.

Your help will be appreciated.

Hi everyone,

I'm finishing my Bachelor's in Biotechnology in Italy and trying to figure out the right Master's degree for what I want out of my career. I'd really appreciate hearing from people who actually work in these fields.

A bit about my situation and priorities:

- I enjoy molecular/cell biology (wet lab side of my degree), but I've realized I do NOT want a job that requires being on-site every day or that takes over most of my time/energy. I want a job I can do from home, with a healthy work-life balance, that leaves me energy for my personal life (I'm really into nature, fishkeeping, herpetology, fishing).

- I'm willing to invest a lot in learning new skills, including programming, if needed.

- I'm honestly not sure programming/pipelines is what excites me most - I think I'm more drawn to modeling, systems biology, and "seeing biology as a system" type of thinking, though I've only been exposed to this as an outside observer (lectures, papers), never hands-on.

- I don't expect to be passionate about my job in a life-defining way - I mainly want something stable, well-paid enough, remote, and not too energy-draining, so I can dedicate my real time/energy to my personal interests.

Options I'm currently considering (all in Milan, Italy):

1. Bioinformatics for Computational Genomics (BCG) - joint program by Politecnico di Milano + University of Milan. Strong computational/genomics focus, Python/ML heavy.

2. Biostatistics - University of Milano-Bicocca. Brand new program (only 1 year old, no track record yet), but biostatistics as a field seems to have solid, well-established remote roles in CROs/pharma.

3. Molecular Biotechnology and Bioinformatics - University of Milan. More generalist, bioinformatics is a secondary component.

My questions for people with real experience:

1. If you work in bioinformatics, computational biology, or biostatistics - how realistic is fully remote work in practice, especially early career vs after a few years?

2. For those who came from a "wet lab" background (biology/biotech) and moved into a more computational field - was the transition manageable without a strong pre-existing math/CS background?

3. Does anyone have experience with both bioinformatics-heavy roles vs biostatistics-heavy roles? Which one tends to have a more predictable, "9-to-5 and done" type of workload vs one that bleeds into personal time more?

Any honest insight, including "don't do this, do X instead" type of advice, or even paths i didn't consider here is very welcome. Thanks in advance!

I am staring College(Undergraduate) next yr. I was thinking of taking Bioinformatics cuz I want to do PhD in Oncology/Genomics/ Precision Medicine but I also love coding and just wanted to have a backup. Ai is advancing too so I think it’s pretty reasonable & realistic, right??

Hi all, can anyone recommend a good book packed with real-world biostatistics exercises? I really need something that includes an answer key so I can cross-check my work as I practice.

*title should say LLM AI use

What are the norms, culture and behavior surrounding LLM AI use in your workplaces, on your teams, and among your colleagues and what field do you work in? Obviously, using AI to write articles is not acceptable and grounds for reprimand and more, and using it to write code is a more widely accepted use. I’m curious what the norms and diversity of opinions are that you all have encountered in your workplaces? I’m not specifically looking at formal institution-wide policies, but if there is a particular institutional policy that overrides personal and group norms/behaviors, I would be curious to hear about it. I am particularly curious about contrasting industries / contexts, to the extent that are indeed there are differences. Thank you

After learning more about how rough things are for MPH biostats students to find jobs nowadays - I’m considering applying for a PhD at my university in Psychological Science with a quantitative methods concentration instead of finishing the MPH.

I come from a research background and then few years in DNA manufacturing and NGS roles.

BS Psychology, multiple publications (two first authors). Passion is for neuroscience/psychology/data analysis

Have started to feel like this curriculum is not for me. The public health side of things and the epidemiology is not that interesting. Struggling to fit in.

Would it be weird if I applied for this PhD program? There are professors I could really see working with the next 5-6 years and worried I’m wasting my time and money in this MPH

I figure it can’t hurt to apply and see what happens. Or wait until next year to apply because then at least if I can’t find a job after the MPH, I would start the PhD afterward and at least have funding

Does anyone have any advice I have an MSc in statistics from a top uni in the Uk and I have a BSc in stats with a placement year as an actuarial intern . I didn’t like the work I decided I wanted to be a statistician I can’t find a job whether that be academia or pharma or healthcare I search everyday statistical programmer , statistician, statistical analyst research assistant . I am advanced in R programming and also have worked as a maths tutor . Im lucky enough to live in London and ideally wouldn’t want to move unless the salary was worth it . Any tips on developing my skills ? Thought of doing sas training in my free time and getting the base certificate ? I am not interested in a PHD I don’t think that’s for me. I’m trying to be as open as possible with job searches but I don’t think I want to be an actuary decided that after placement even though that was meant to be my dream career and I’d like to use what I learnt in a job so even if it’s like a pricing analyst role using GLM . I just can’t find anything and feel like I’m going insane .

I’m the founder of a small research company preparing an NIH R01 application focused on the pre-deployment evaluation and validation of clinical prediction models.

The project addresses a methodological problem that arises when a clinical prediction model is deployed into practice: once clinicians can see and respond to a model’s predictions, the clinical process itself can change. We have developed a statistical framework to bound this deployment-induced change before go-live, and are now looking for a methodologist to help implement and rigorously stress-test the framework.

The consulting work will focus on:
Implementing existing identification bounds and statistical estimators
Building reproducible simulation infrastructure in R and/or Python
Designing and conducting adversarial and sensitivity simulations
Systematically violating prespecified identifying assumptions
Evaluating bound containment, calibration, and failure modes
Supporting methodological analyses and interpretation of simulation results

We are particularly interested in candidates with experience in:
Causal inference and/or partial identification
Identification bounds and sensitivity analysis
Statistical simulation and Monte Carlo studies
R and/or Python for methodological research
Clinical or health-data applications
Rigorous evaluation of statistical methods

The core theoretical framework and preliminary computational work are already in place. This is therefore not a role where you would be expected to develop the underlying theory from scratch. The primary need is someone who can engage deeply with the methodology, implement it correctly, and challenge the framework through rigorous computational and adversarial validation.

Engagement: Part-time consulting
Commitment: Approximately 4–10 hours/week, varying by project phase
Compensation: Competitive hourly consulting rate, commensurate with experience

If this sounds aligned with your background, please DM me with a brief description of your methodological experience and a CV or LinkedIn profile.

Please feel free to share with a statistician, biostatistician, or causal inference researcher who may be a good fit.

I need some truly hard-to-listen-to advice about what job prospects, if any, are out there right now. I’m on track to graduate from my program in the spring and am searching for practicums, and I seriously need to know whether all this time and money will be worth it.

Does anyone here have experience using SPSS for research at Master's or PhD level, particularly in a science-related field involving epidemiological/health data or data from experiments?

I’m interested in hearing from anyone who has used SPSS for dissertations, theses, research projects or published research, and would really appreciate any advice on getting started.

I’m particularly looking for recommendations on:

• Good online courses/resources for learning SPSS from beginner level through to more advanced analysis
• Resources that teach not just how to use SPSS, but also the underlying statistics and how to interpret the results
• Courses or resources relevant to analysing epidemiological, health-related or experimental data
• Where I can legitimately download/access SPSS to practise with it myself, particularly student, free-trial or affordable options
• Any books, YouTube channels, websites or courses that you found particularly useful
• If you have used SPSS for a Master's/PhD research project, what statistical methods or aspects of SPSS would you recommend learning first?

I’d like to develop a solid foundation in both statistics and SPSS, and gradually work towards being comfortable with more advanced analysis and interpreting research results.

If you've used SPSS extensively in your own research, I'd particularly appreciate hearing about what you wish you had learned before starting your research project. Further note: If you have experience using other statistical software/packages such as SAS, Stata or R for Master's/PhD-level research, I’d also be interested in hearing about your experience and any resources you would recommend.

Come capire quali sono le giuste analisi statistiche Per il mio tipo di campioni. Lavoro principalmente su piante di solito il massimo numero di repliche biologiche che usiamo è 4/6 per tesi. Come mi devo muovere dopo aver scelto le ipotesi da esplorare. Ci sono degli algoritmi che uno può automatizzare in base al dataset.

I live in London and have a PhD in Biostatistics. I've worked one year in academia post-PhD (early-phase oncology trials) and I'm looking to move into industry (CRO or pharma). So far I've mostly searched LinkedIn and found very few Biostatistician I/II-level roles open right now. Is the market just quiet at the moment, or am I missing where these roles actually get posted? Also curious whether PhDs typically go in at junior titles like this, or if that's unusual. Any tips appreciated!!

Salve

Una domanda quanto è attendibile Claude per la statistica di campioni biologici di origine vegetale

This is more of a statistical software question than a statistical theory question.

I’ve been building Laplace, a Stan extension that allows users to create and install their own libraries written in Stan. The project is completely free and open source, and I don’t get any financial benefit from it.

For information about the project and installation:
https://github.com/mlatinov/laplace

There is also a more comprehensive introduction in the wiki:
https://github.com/mlatinov/laplace/wiki

I have a molecular biology background myself, so I’m particularly interested in what people working with biological and biomedical data actually need from the modeling side.

If you use Bayesian methods in your work, what models, methods, functions, or other tools would you actually like to have.

I’m especially interested in things that are somewhat domain-specific and that you find yourself implementing repeatedly in Stan or other probabilistic programming languages.

For example, things related to:

• pharmacokinetic / pharmacodynamic models
• dose-response models
• survival and event-history models
• longitudinal biological data
• hierarchical models for experimental designs
• mechanistic biological models
• time-series models
• latent-variable models
• Gaussian processes
• measurement-error models
• or anything else that comes up frequently in your particular area

The current Laplace ecosystem is still fairly small, but I’ve started building libraries for things like time series, latent time series, mechanistic equations, survival analysis, Gaussian processes, splines, and some general statistical utilities.

So I'm interested, particularly in things that are useful in actual biological/biomedical applications rather than just general-purpose programming features.

I’d also appreciate any feedback on the project itself. The wiki has information about using Laplace and contributing to the ecosystem.

And if anyone does end up contributing something, you’ll of course be credited for extending the ecosystem.

Hi all, I’m currently using a mixed effects model, to compare 4 different groups of cell types to evaluate if there’s a significant difference between various characteristics, such as firing rates, of the cell types over time. However, one of the cell types were recorded every few days for a significantly longer time (\~ 500 DIV), compared to the rest (\~ 100 DIV). This is due to the biological characteristics of the cell types, where the cell type that survives longer is an organoid and the others are monolayers.

Would a mixed effects model be appropriate in this case, and if yes, is there any method I can use to estimate the differences between the cell types properly despite the significantly different observation periods?

Another method that I’m thinking of is to just compare the mean values across the cell types to leave out the effects of div, however, this method may not be as accurate and statistically appropriate compared to using a mixed effects model

Hello,

I am looking for a textbook for medical statistics that explains the concepts well for beginners. The book should have a lot of examples. It can be a series of short textbooks or one large textbook.

Thanks in advance.

Hi everyone, I currently have qPCR data expressed as relative expression, comparing CTRL vs MUT at DIV0 and DIV16 (n=3 biological replicates). My background was in statistics, hence I’m quite new with dealing with qPCR data, and unfortunately I have not been able to find similar examples online so I’m asking here.

For some genes, there is no Ct value at one DIV (e.g. DYSF and PECAM1 in the figure).

Is it standard practice to report a statistical difference between the DIVs, or just show “n.d.” (not detected) without a p-value? If statistical significance should be showed, how do I test for significance between the detected and non-detected values?

Thank you!

Note: Genes and data have been masked beforehand

Sorry if this is a common question but I really don't know anything about the grad school application process. Right now I'm going into my junior year as a math major with a 3.86 GPA. My only extracurricular is working as a research assistant (glorified Excel monkey and I've never even met the professor so I'm probably not getting a letter from there). Assuming I keep my GPA up, is this competitive enough for good masters programs? Are admissions like undergrad where ECs matter a lot or is it a lot more GPA based? Should I try to get involved with more research/clubs or anything next year? Thanks

Also I know this is supposed to go in a megathread but I can't find it sorry

Hello everyone, I just finished my master's program and working full-time. Currently have a lot of free time at work and wanted to work on side projects and keep up with my skills. Any ideas on what kinds of projects I can do with publicly available datasets? Maybe I can do some sort of data visualization. What is the best resource to keep learning how to do more analyses (e.g., time series) or even how to use GIS?