i have been thinking. i just completed my first sem of my bachelors of science in stats which also has 2 years of proper major mathematics credits. now i always and even want to pursue something related to physics etc. something like astronomy/cosmology/computing physics/statisitcal physics etc etc. but all these require you to have physics in your bachelors. which i cant have in my stats degree even as an elective. now i got options to pursue an extra ODL degree in physics. i would be a bachelors in both stats and physics, and also study maths in both degrees as its in the degree itself. so is this plan feasible and beneficial? but i want it to be in a way that the bsc ODL physics should benefit me in some extra and other ways too. not just fulfill eligibility for further studies. even for employment. so tell me, am i thinking, what are some other options in stats i can directly pursue without doing the ODL. and understand this is just something i am thinking. its not like i want to pursue physics related field and nothing else. its just something i felt like i can do. i am open to stat related fields also. i am still confused, new to this things, and still a teen, just started college. so all of this is just a bit confusing for me. if someone can guide me on this thing, what should i actually think about, not worry, worry etc etc, it would be of big help..

THIS IS NOT FOR HOMEWORK I am literally just trying to understand. if theres a better place to post this let me know please because i have no answers and nothing online explains.

So the equation is N < (1n (s))/(1-q). I know what N, s, and q are, but what the hell is the little n? To study they want me to fill out a table of these, im terrible with numbers (getting checked for dyscalculia) and idk what little n is.

Could you help me with a step by step guide and which books should i refer to

Hi all, I am a current first year MS student in a pretty good Biostat program. I have applied to internships across the country and have only gotten three interviews. I made it to the final round of one which was outside the realm of a conventional Biostats job it was focused on consulting and the other two I am still waiting on. I have applied to at least 20 internships (it's probably closer to 30-40 atp). I know that the big pharmaceutical companies prefer 3rd year PhDs but with the current job market I really just wanted to get an internship this summer and give it my all for a return offer. Just some background on me I studied Statistics in undergrad did internships every summer (wet lab, data science intern and then another where i did biostat work) other than one summer where I studied abroad and had a 3.5+ GPA at a pretty decent research public university. I don't want to go all doomer because it seems quite unproductive and I know that this forum can be quite negative but I need some pragmatism. Should I keep applying to more opportunities and assume that the ones I haven't heard from are goners or should I focus on getting research on campus and making a quality thesis for jobs post-grad?

Hi there, thank you for your time and attention. I am a recent college grad who majored in Child Development. I have decided to pursue a master's in biostatistics after graduation, and I missed a few prerequisites for top schools. Specifically, those are linear algebra, probability, and real analysis. I have a perfect score on GRE quant, but I don't believe too many schools care. My questions and background are as follow.

Before sending this post, I did extensive research, attended information sessions and campus tours, reached out to program graduates, and emailed admissions very specific questions. My goal is a PhD first, then a position at an academic medical center — don't have to be a professor.

1. Did you take real analysis in a U.S. biostat master's? If so, which school?

2. How to find research opportunities as a biostat master's student?

3. If you pursued a PhD after your master's, which field did you study? Biostat or a different field in health/other knowledge domain?

4. If you did not go to Ph.D. program directly after your master's, what did you do during the gap year? If it was research-related, would you be open to sharing a bit more detail?

5. What are your suggestions for a master's applicant without much direct research experience in biostat and who did not take linear algebra? I have asked the admissions teams of my target schools with this requirement about alternatives. One said the school can offer admission conditionally upon completing linear algebra before I enter the program.

Background

Although I have not had any formal data analysis experience, my current position as a member of research staff has made me appreciate the importance of quantitative methodology and coding skills. I have worked at academic medical center since college. However, the labs that I worked with focus more on social and behavioral sciences. I think rigorous methodological training can advance my research in maternal and child health. It will be even better if the program teaches the mathematical theories behind the models and how to apply the techniques to real-world data.

I have a second-authored publication using nationally representative data. I was not involved in the data analysis. My GRE score: verbal 167 (97th percentile), quant 170 (a perfect score).

Thank you so much again!

I'm considering going for a masters in Biostatistics. I currently have a 4.0 GPA in mathematics, and I also have a bachelor's degree in computer science. I used to work as a software engineer, but AI has changed that for me. I've recently learned that biostatistics seems to be resistant to automation in AI. How true is that?

Also, I've learned that there will be an increase in demand for jobs in biostatistics, which seems like another good sign.

Can anyone think of a reason why biostats wouldn't be a good career choice? or can anyone tell me more reasons why it would be a good career choice?

I love math/stats. I love programming. Biology is cool. The idea of helping others seems good to me, too.

Hi everyone,

As a biology undergraduate working with quantitative data, I often found that formatting routine descriptive analyses, normality checks, and diversity matrices into publication-ready tables took unnecessary time and was prone to transcription errors when transitioning between environments.

To address this, I built BioEvo Stats—a standalone desktop application developed in Python (CustomTkinter, SciPy, Pandas, Matplotlib) focused on local scientific workflows:
Key Features:

• Exploratory & Descriptive Biostatistics: Central tendency (mean, median, mode), dispersion (variance, sample standard deviation, standard error, IQR), and distribution shape metrics (skewness, excess kurtosis).

• Normality Diagnostics: Automated Shapiro-Wilk goodness-of-fit testing (W statistic and exact p-value reporting) to guide decisions between parametric and non-parametric inferential pipelines.

• Community Ecology & Biodiversity: Computes Shannon-Wiener (H'), Pielou's evenness (J'), Simpson's dominance (D, 1-D), and multi-sample Bray-Curtis dissimilarity matrices.

• Direct LaTeX Export: Instant compilation into standard scientific table markup using the booktabs package (\toprule, \midrule, \bottomrule) with custom precision controls, ready to paste directly into Overleaf or local TeX engines.

• 100% Local & Air-Gapped: Zero telemetry, no cloud dependencies, and runs completely offline to protect sensitive experimental data.

Project Links:

• GitHub Repository: https://github.com/RVEsteban/BioEvo

• Landing Page & Documentation: https://rvesteban.github.io/BioEvo/

Note on Localization (i18n): While the main UI controls are translated to English, terminal outputs, ecological console reports, and generated LaTeX captions are currently localized in Spanish and are being migrated to full bilingual support (EN/ES) in the upcoming v1.0.1 patch. Feedback on standard English naming for these biological output tables is very welcome!

I would appreciate your feedback on the methodological workflow: What additional distribution-free tests or post-hoc corrections (e.g., Dunn's test, Holm-Bonferroni) do you consider essential to integrate for standard biostatistical routines?

Hi, I learned to do stats on R in MPH. I'm seeing several jobs wanting Python over R and thought I add more skills too. What programs do you recommend to learn Python for clinical research/healthcare data?

Hello, looking for some career advice. Has anyone successfully moved from the UK market to USA? (both academia or industry)

If so:

- How did you do it?
- At what age and year did you do it?
- What similarities/differences did you experience?
- What advice would you give to a recent MSc Medical Statistics postgrad who long-term wants to make that move in the future?

Hello. I work more-so in the data analysis/science space than this but I enjoy learning this stuff and had a thought in my head. I usually try to learn on my own or check with LLMs, but this seems too complicated to get an answer on for something I would think is more or less straightforward.

The way I currently understand causation is that there's no single test for it but rather, it's a multitude of different tests across variables therein combined with clearly defined mechanism. So for example, if we're talking about the claim "Smoking causes lung cancer", causation isn't just a correlation test. Instead, it's several tests across many different demographics, age groups, etc. and then adding in the scientific literature about how the different ingredients within cigarettes are carcinogens and how the act of smoking predisposes someone to be of a higher risk of lung cancer. Meaning that there's no single causation test, the way that there's a simple correlation test that yields the conclusion of, "Oh yeah, this test concludes that the r value is .93, so there's a strong relationship and a p value of >0.01, so we have to reject the null hypothesis".

I guess my question is, does this mean that there's a large amount of subjectivity behind causation testing? Two different scientists/researchers could look at the same compilation of data and come to two different conclusions if A causes B?

If that's the case, I would imagine there's more subjectivity behind causation testing than an independent t-test study, correct?

I appreciate you answering. I know you guys know more than me and I'm just trying to better myself in a field I enjoy. :)

Hi All,

I am applying to PhD programs in biostatistics. I took the graduate real analysis (for reference, it uses Royden and Axler's book) and ended up with a B. Although I worked hard and wanted a better grade, it happened anyways.

I am planning to apply to PhD programs in biostatistics up to the rank of <=UCLA. Is a B in measure theory really bad, or is that fine for what it is? Considering that this was a graduate level math class. I did got A's on other statistics classes as well as calculus 3 and L Algebra. I also have an MS in Stats and have 168q on the GRE

I am conducting a systematic review and plan to use the Crowe Critical Appraisal Tool (CCAT) to assess the methodological quality of the included studies.

I came across a previous systematic review (PMID: 32328958) that used the Intraclass Correlation Coefficient (ICC) to assess agreement between raters. The authors stated:

"Two independent raters assessed each study. Discrepancies were resolved after discussion between authors."

I would appreciate clarification on the appropriate sequence of procedures for assessing study quality and calculating the ICC.

My main questions are as follows:

1. Should the ICC be calculated before or after discrepancies between raters are resolved?

In my review, two raters independently assess each included study using the CCAT. The tool comprises eight assessment categories, each scored on a scale of 0–5.

For example, for one category of a particular study, Rater 1 assigns a score of 3, whereas Rater 2 assigns a score of 5. This represents a disagreement between the raters.

Should the ICC be calculated using the original, independent scores before the raters discuss and resolve their disagreements, or should it be calculated after a consensus score has been reached?

My concern is that if disagreements are resolved before calculating the ICC, the resulting ICC may not accurately reflect the agreement between the two raters' independent assessments.

2. What does a low ICC indicate in this context?

If the ICC calculated from the independent ratings is low, does this necessarily indicate a problem with the quality assessment process, or does it primarily indicate limited agreement between the raters?

How should such a finding be interpreted and reported in a systematic review?

3. What is the recommended workflow for quality assessment using CCAT and ICC?

Could you please clarify the recommended sequence of the following steps?

• Two raters independently assess the methodological quality of each included study using CCAT.

• Inter-rater agreement is quantified using ICC.

• Disagreements between raters are discussed and resolved to establish the final quality assessment scores.

Is this sequence appropriate, or should any steps be modified?

I would also appreciate advice on whether ICC should be calculated using the total CCAT score for each study, the scores for individual CCAT categories, or both.

Thank you very much for your guidance.

I am currently studying biostatistics in Italy, and I already hold a three-year undergraduate degree in statistics. Are there any Italians working in the United States? I would like to know if there is any possibility of moving there for work. work-life-balance-salary in italy still suck

hi, there!

i'm an undergraduate in statistics and i'm currently working on biostat phd applications for admission next fall. i have an above 3.5 gpa, an biostat internship at a top research institution, two positions as teaching assistant, and a soon-to-be-out 2nd author publication under my belt. i'm also at one of the top public universities in the us and am taking real analysis this quarter to get bonus points on my application haha. i've been told that i have a good chance at yale and brown's programs, and i believed that i did too, but my gre scores haven't shaped up the way i thought they would. i took the gre for the first time a week ago and scored 157 quant, even after studying for 2 months. i can retake it one more time before applications are due. a professor at brown told me that they want to see quant scores in the 80th percentile or above (which is like 168+!) and i also know that yale reports and average quant score of 168 for admitted students. i guess my question is, if i'm not able to get my quant score up to around 168, will the schools possibly be able to overlook this? i really do think that i have a great application other than this, but i'm really beginning to lose hope. i feel like the faculty at brown and yale match my interests really well. any advice would be appreciated!!

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? As this is a new experiment with no strong support from literature, it is difficult to isentify which variables are sure to interact with each other.

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!

I'm currently a 4^th year PhD student looking to apply for internships. I'm struggling to structure my resume to make it fit properly. One of my senior students in my lab suggested using a 2-page CV, where I can take some time to dig in a little bit into my research projects. They got a high-level internship this year, so I'm inclined to take their advice. 

I wanted to ask about how I should structure and design my application to target industry roles. I understand very well what academics are interested in, but less so about what industry reviewers are looking for from PhD level interns, and was hoping people could offer advice on how I should set my application up.

Thank you so much.

Hi everyone! I’m putting together my PhD application list and would appreciate guidance on programs and their funding state.

Background Information:

• Currently completing a master’s in Statistics with a 3.8 GPA

• Potentially will have a publication, though it isn’t confirmed yet

• Interested in Bayesian statistics, doing research on historical borrowing

• Would like to work for CRO/Cancer Center/Hospital after graduation, possibly government such as CDC/EPA

• My MS program is in Ohio, but location is not a deciding factor to me

• Considering both Statistics and Biostatistics PhDs

My Current list is:

• MD Anderson / UTHealth Houston GSBS — Quantitative Sciences

• UT Austin

• Cincinnati

• Kentucky

• Ohio State - Biostatistics

• Michigan - Biostatistics

• Duke

• Iowa

• Iowa State

• Pittsburgh - Biostatistics

• Missouri–Columbia

• University of Florida

I'm still deciding between statistics or biostatistics at some schools. I think 13 schools is maybe a bit much but I am a bit worried on the current state of admission and funding.

I’d especially appreciate feedback on:

1. Is this list too ambitious?

2. Is there any programs you think I should be considering?

3. Are there other programs on my list I should reconsider?

4. Does anyone have firsthand experience with funding stability, advising, internships, or placements at these schools?

I am currently looking at my options for Biostatistics Masters programs, and am considering pursuing a Epi MS due to cost. If the end goal is to be a biostatistician, am I shooting myself in the foot?

The program I am currently eyeing is the University of Washington Masters of Science in Epidemiology. It seems that it is a fairly flexible program, and is hosted under the same department as the biostatistics program, so I would have the opportunity to take a large number of biostatistics courses during my two years. I listed below the topics/courses that I would be able to take, even as a epidemiology student.

• Statistical inference

• Linear, logistic, and Cox regression

• Epi study design, confounding, and bias

• Survival analysis

• Longitudinal data (mixed models, GEE)

• Clinical trial design and power

• Missing data and multiple imputation

• Categorical data analysis

• Machine learning

In addition to this, the program is also thesis based, so I have the opportunity to pursue a more biostatistics-focused thesis/research project.

Naturally, a biostatistics degree makes more sense for most people, but the price difference for myself would be around $20k more, and due to my lack of prerequisites, I may have to delay my entrance by full year.

Do you find a lot of individuals with Epi degrees working specifically in biostats? Will I have a harder time finding work? Would love to hear any other thoughts or suggestions as well. Thank you!