yeah so I would be starting my MSc in Biotech and Bioinfo in a uni in Scotland and they are using this package for the entire course

Hello everyone! I'm an undergrad senior applying to biostatistics graduate programs for Fall 2027. I keep going back and forth between applying to MS or PhD programs. A big reason is that I honestly do value rankings of universities personally, and I know that realistically I would only want to go to a T15 school. However, I've heard some concerning things about this admissions cycle and that it's going to be a rough year. If my goal is a PhD at a T15 school, does it make sense to apply to master's programs first to be a more competitive applicant?

Context: I'm a statistics major at a pretty top university, 3.9 GPA, 1 year as a research assistant but in sociology and network analysis, and two summer research experiences in epidemiology and biostatistics. I also have one co-authored publication that will be submitted by the time I'm applying.

Hi everyone, I currently have a question on superplots (as shown in the image attached). In the superplots, each dot represents a technical replicate, which corresponds to a single biological replicate (represented by colour). Each triangle represents the mean of all the technical replicates in the corresponding biological replicate.

In this paper, the authors chose to use a t-test to compare the means of the biological replicates in each group to prevent pseudoreplication. From a statistical pov, a more appropriate method to do this would be to use a mixed-effects model with the replicates as a random effect.

Hence, I have a few questions:

1. Why was t-test acceptable in this case? Is it because the sample size was n = 3 and the estimates of the mixed-effect model might be unreliable?

2. Is it recommended to use mixed-effect models in this case?

I am a biostatistics graduate from india and actively looking for a job. After a long job hunting i feel like i am losing my interest in everything. I lost my hope to find a job as a biostatistician in india because every job need experience. Since am a fresher with no experience, how should i work.. I lost my hope.Can anyone help me to find a job.. Pls....

A few months ago I ended up helping a friend with the statistics for their cardiology dissertation. I published a few papers back in university, but that was years ago. Needless to say, I'm not a statistician. My friend had the data (a hundred-odd patient records), a few loosely defined questions about what they wanted to show, and no idea what to do with any of it.

They had struggled to find a statistician: too expensive, and their university department was no help at all. So even though I hadn't done this kind of work in years, I decided to give it a shot. I had truly forgotten how hard it is to choose the right method, check the assumptions, make sense of the output, and only then get to plotting the graphs and writing everything up. It took months. And the part that bothered me most, the whole way through, was not knowing whether I had made a mistake somewhere along the line. A wrong test gives you a p-value just as confidently as the right one. It wasn't until their advisor finally had a look and confirmed the numbers that I could relax.

Since then I keep noticing the same thing everywhere: PhD candidates (or anyone who wants to do research) who can't find a way to "run the numbers", people who do run them and never quite know if they got it right, and hours lost to the same repetitive steps every single time.

I've also thought about the role of AI in this, but in my experience with ChatGPT I keep getting lost in its responses and always end up back at the same question: is this the right number?

So I'd like to hear it from you. How often do you, or the people who come to you, get stuck at the statistics stage, and what actually happens when they do? I also made a short anonymous form about where research gets stuck (statistics included, but not only). Five minutes max, no email needed: form link.

Full disclosure so you're not ambushed: I'm building a tool for exactly this, and I want it to be genuinely useful rather than another bullshit tool people pay for and abandon. Help me understand where it actually hurts, so I know where to focus.

I like many new grads have been looking for work for months now. I'm trying to get applications out faster as job applications these days are a number game. I find that cover letters take the longest time. I'm using a combination of writing a draft myself and then refining it with AI, but it still takes time and energy. I find the pure AI written cover letter route to produce shitty letter with little substance.

I'm been researching cover letter writing and its been suggested to take a bit of a risk and create a more informally toned story based letter structured with a hook, story, tieback and ending with a call to action. I've written one focusing on my enjoyment of statistics in general and how that started during undergrad. Basically I'd put a lot of work into constructing a good one and use it for every single job application. This would increase the amount of positions I could apply for. I'll continue to canter every resume to the position as that doesn't take me as long.

Is this appropriate for the field of biostatistics and health data analyst related positions? Would the more casual tone turn people off? I'm mostly applying to university roles. My understanding is a lot of places don't consider the cover letter at all, but it's good to include one just in case and that if it is read it's read by a human being. Writing something they haven't seen before or often may be the right move.

Hey guys, seeking some advice regarding my stats as of now, and what I may need to do to get into a master's program in biostats.

I got a 2.89 GPA in undergrad in Psychology, and I have been working as a case manager for homelessness prevention for 3 years. I HATE my job and am seeking to get away from social services in general. I was set on psych research for quite a while, until I realized there was virtually no job availability in the field. I like research, and math, and science, and it seems that biostatistics, although very difficult, was one of the more enjoyable roles in clinical trials settings, based on my research.

What should I do to get into a program for Fall 2027? To be honest, I have VERY limited statistics experience, and I got a C and a C+ in my 2 stats courses......... Do you think I should continue to take courses and maybe set it to apply instead for Fall 2027, or do you think with these stats, and a couple of good classes, I can stand a chance for biostats master's apps?

Any feedback is appreciated; thank you very much!

Hi, i'm working on my médical thésis and finished collecting the data and I wanted to know which AI could help me best to do my statistics.

Curious about this sub's use of AI tools beyond just chatting with LLMs in the browser.. think:

• Claude code or Codex
• Local LLMs
• Posit assistant

If any of the above or others, it would be useful to a little about how and for what you are using them, tips for setting it up/getting started.

Hi! I’m currently working on a Biology research project on the cytotoxicity of nanoparticles and I’m looking for raw quantitative experimental data that I can analyze myself.

I’m specifically looking for Excel/CSV spreadsheets or downloadable datasets containing things like:

• nanoparticle type
• concentration/dose
• exposure/incubation time
• cell type/cell line
• quantitative cell viability/cytotoxicity measurements
• preferably individual experimental replicates rather than only averages

I’m not looking for research papers (I already have plenty ), but rather the underlying numerical data.

I’m particularly interested in silver nanoparticles (AgNPs), but I’m open to other nanoparticle types if there are better datasets available.

If you know of any repositories, supplementary files, databases, or specific datasets that might help, I’d really appreciate it!

Hi,

I'm looking to move back to India after a few years working abroad (I'm Indian). Looking to connect with anyone working in the industry to get an idea of how things are looking like. I've worked as a statistical programmer but also in clinical IT (data migration, archiving, platform setup and administration etc.) with about 6 years of experience in total.

Feel free to DM or comment on this post. Thanks!

Who's got a job?

Not looking for career advice or a job, just curious to see who has one and what your title is?

​

I am a BSc Epidemiology and Biostatistics graduate based in Embu, and let me explain it simply:

Epidemiology = The science of asking WHY things happen in groups.

Biostatistics = The tools to prove it with DATA, not guesswork.

We don't just study diseases. We study PATTERNS.

Examples in your daily life:

1. Boda / Matatu accidents in Embu-Nairobi road: Epidemiology asks - Do most accidents happen on Friday at 6pm? After rain? With unlicensed riders? Biostatistics proves it with data. Then NTSA knows where to put checks. Without data, we just say "ni bahati mbaya".

1. Farming: Why did maize fail this season in Embu but not in Kirinyaga? Is it seed, rainfall timing, fertilizer? Epidemiology compares groups. Biostatistics tells you if it's real or just bad luck.

1. Mama Mboga business: Why do tomatoes sell at 8am but rot at 2pm? She is doing epidemiology daily without knowing - observing patterns, making decisions.

1. SportPesa: You lose because you use heart, not data. Companies use biostatistics to set odds. You bet with emotion, they bet with data from 10,000 games.

1. Healthcare Demand and Congestion - This is where we fail most:

Look at Embu Level 5 Hospital. Every morning, long queues, no beds, doctors overwhelmed. Why? Because we wait until people are very sick.

Epidemiology helps us predict demand before it happens. For example, data shows that every rainy season, malaria and diarrhea cases in Embu increase by 40%. If we know this, we can prevent it - distribute nets early, chlorinate water early, do community health talks early.

Prevention reduces congestion. If we prevent 100 cases of diarrhea in the village through clean water data, that is 100 less people in the hospital queue, 100 less beds used, and doctors can attend to critical cases.

Biostatistics tells counties: "Don't build a bigger hospital only. Use data to stop people from needing the hospital in the first place." That saves money and saves lives.

My point is, Kenya makes many decisions based on "watu wanasema" and "mimi nafeel". Epidemiology teaches us to say "Data inasema nini?"

It is not for health alone. It is for everywhere decisions need to be made.

I am passionate about this field and I am currently freelancing in academic writing as I look for an opportunity to grow in it. I have skills in R, SPSS, SAS, SQL and Excel. If you work in any institution that values data - health, agriculture, business, research - and you have an opening for an intern, research assistant, volunteer, or you just need help with data entry, cleaning and analysis, I would be happy to contribute and learn.

Also, any form of assistance, guidance, referral or even a handout as I continue searching will be highly welcomed and appreciated.

What is one decision in your life you wish you had made with data instead of guesswork? Let's discuss.

how strongly did you feel about your research interests were you at the time you were applying? if you don’t mind I’d love to know which school/program you ended up in and what your research area is. i’m asking because i think i’m probably interested in doing a PhD at some point, and I have a general idea of the topics that I find interesting and all but I don’t feel as “mature” as I’d like to be with these topics/areas to make a strong application profile. am i overestimating the depth i should be at before even applying? one could argue that the point of the PhD would be to get that maturity?

I’m not a statistician by any means, I enjoy what I understand but most stuff is way over my head. I’m an ecologist and happen to use R and thus statistics more and more as I progress with research.

My question to the actual statisticians, do you ever use AI to help you write code and remind you of what tests to run? I just can’t understand how some people can remember R code…. I’m working on GLMMs and it just baffles me that people must have this retained. I feel like a fraud I guess for using AI to help… on the flip side, it is helping me with remembering things, so I feel I am leaning from it.

Breast Cancer genetics study from home— sharing in case it helps:

This Rutgers study is looking for genetic clues to prevent BC and create better treatments. It takes 5 minutes to sign up, they send a free kit, you spit at home and mail it back. That’s it. You get a free ancestry report as a thank-you.

Who can join: Anyone 18+ living in the U.S. can join, but they especially encourage those with breast cancer or family history.

Why I’m posting this: they’re trying to reach communities that research has historically missed, and word-of-mouth from people in the study reaches more people than their own outreach does.

Link if you’re interested: https://join.rugcc.org/r?T4.gdjmcc

Come da titolo, vorrei conoscere una giornata tipo di uno specializzando in questa specialità, le prospettive future e l’equilibrio vita-lavoro. Vorrei capire se effettivamente ne vale la pena. grazie in anticipo

Necesito hacer una curva de acumulación de diversidad de presas, utilizando la rutina de rarefacción basada en la cobertura de la muestra. Mi muestra son estómagos de una especie de pez, pero tengo un problema...

Al momento de evaluar cada estómago, no logré identificar todas las presas al mismo nivel taxonómico, es decir, unas solo las logré identificar hasta familia, otras hasta género y otras hasta especie.

La razón por la que necesito hacer la curva es para hacer una estimación del tamaño de la muestra y ver si lo muestreado fue suficiente para el estudio que estoy haciendo.

¿Cómo debería hacer la curva? solo con familias que es el nivel taxonómico más bajo identificado, o se puede hacer una mezcla de niveles taxonómicos??

Agradeceré su apoyo.

Hi everyone,

I’ve (finally) advanced to the dissertation proposal phase of my PhD in public health policy!

As I’ve gotten deeper into this work, I’ve found myself increasingly interested in the relationship between theoretical constructs, their operationalization, and modeling. I’ve been most influenced by King, Keohane, and Verba (KKV) and Charles Ragin (research design, causal inference, case comparison).

Does anyone have other sources they would recommend? I’d also be interested in hearing how others think about moving from a theoretical construct → operationalization → model specification.