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Award-Winning Biostatistics Tutors
Certified Tutor
8+ years
Amanda
Most biostatistics struggles come down to not knowing which test to use or why — is this a chi-square situation or a t-test, and what does the p-value actually mean? Amanda's Master of Public Health training required heavy coursework in epidemiological statistics, so she teaches biostatistics with t...
The University of Alabama
Bachelor of Science, Biology, General
Baylor College of Medicine
Doctor of Medicine, Public Health
Certified Tutor
14+ years
Jason
Having completed pre-med coursework at Bryn Mawr and then medical school at Penn, Jason encountered biostatistics where it matters most — evaluating clinical trial designs, interpreting odds ratios in journal articles, and assessing whether a study's methodology actually supports its conclusions. Hi...
University of Pennsylvania
PHD, Medicine and Education
University of Pennsylvania
Master's degree in Education
Yale University
Bachelor's degree in History
Certified Tutor
10+ years
Nina
Nina is finishing a doctorate in biostatistics at NYU after completing her master's at Columbia, which means she lives and breathes this subject — logistic regression for clinical outcomes, survival curves, study design for epidemiological research. She was a teaching assistant in Columbia's biostat...
Columbia University
Masters in biostatistics
Northwestern University
Bachelor of Arts in biological sciences (focus in neurobiology)
Columbia University in the City of New York
Current Grad Student, Biostatistics
Certified Tutor
Applying to medical school while pursuing a Master's in Public Health means Jakobi is knee-deep in the kind of data analysis biostatistics courses demand — study design, hypothesis testing, and interpreting results in health contexts. His biology degree gives him the scientific grounding to explain ...
Princeton University
Bachelors
Certified Tutor
Natasha
Engineering coursework at MIT forced Natasha to build statistical models from biological and chemical datasets — the kind where understanding variance, distributions, and experimental design isn't optional but essential to getting meaningful results. Her chemical and biomolecular engineering backgro...
Johns Hopkins University
Bachelor of Science, Chemical and Biomolecular Engineering
Certified Tutor
8+ years
Ruth
Three years as an ESL instructor and a summa cum laude biology degree taught Ruth something most tutors learn the hard way — explaining quantitative concepts clearly matters as much as understanding them. Now in medical school, she breaks down biostatistics topics like study design, sensitivity and ...
The University of Alabama
Bachelor of Science, Biology, General
The University of Michigan
Doctor of Medicine, Alternative and Complementary Medicine and Medical Systems, General
Certified Tutor
8+ years
Emily
Between her biology major, math minor, and four years of medical school coursework in community health and preventive medicine, Emily has encountered biostatistics from every angle — interpreting clinical studies, running analyses on biological datasets, and applying concepts like sensitivity, speci...
Indiana University-Bloomington
Bachelor of Science, Biology, General
Indiana University-Purdue University-Indianapolis
Doctor of Medicine, Community Health and Preventive Medicine
Certified Tutor
Courtney
Courtney's graduate research in aquatic ecology means she's wrestled with the messy, real-world datasets that make biostatistics click — figuring out which test to run when sample sizes are uneven, or whether a correlation in field data actually holds up under regression. That experience analyzing e...
Arizona State University
Master of Science, Biology, General
University of Notre Dame
Bachelor of Science, Environmental Sciences
Certified Tutor
6+ years
Ingrid
Ingrid's biomedical engineering coursework at Northwestern — including undergraduate research in the John Rogers Lab — gave her hands-on experience designing experiments and interpreting the statistical methods that underpin clinical and biological research. She breaks down concepts like survival an...
Northwestern University
Bachelor of Science, Biomedical Engineering
Certified Tutor
8+ years
Sanjul
Medical school trains you to read studies critically — picking apart odds ratios, questioning sample sizes, and spotting when a confidence interval undermines a paper's bold conclusion. Sanjul, now in his final year of osteopathic medical training with a biology foundation, brings that clinical lens...
Cleveland State University
Bachelor of Science, Biology, General
University of Medicine and Health Sciences
Doctor of Medicine, Osteopathic Medicine (DO)
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Sam
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Statistics Tutor • +26 Subjects
I am a graduate student at the Harvard School of Public Health living, working, and studying in Boston, MA. I have a passion for people and for learning. Specifically, I have studied Spanish for ten years, and graduated from Bucknell University with a degree in Spanish in 2014. I have served as a teaching assistant for a class of ESL kindergarteners and have lived abroad in Costa Rica and Mexico. I am comfortable with students of all ages and ability levels in Spanish, and also available for ESL tutoring. I also have a degree in Biology, and my current graduate school program and research incorporate a lot of statistics. Therefore, I am also comfortable with tutoring in high school and undergraduate level biology and statistics. I am available most evenings and weekends. I can work online or in person in the Boston area. I am flexible to work with your schedule! Hobbies: swimming, writing, reading, cooking, traveling, music, running, hiking, art, travel, books
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I'm a rising Junior at the University of Chicago studying Computational Neurosciences and Fundamentals: Literature & Philosophy. I've tutored 7th and 8th graders in Basic Algebra and essay construction and taught undergraduates Biostatistics and the literature of Valdimir Nabokov, so I'm prepared to work with any age or skill level. When I'm not tutoring this summer, I'll be doing electrophysiological research at NYU's Center for Neural Science and preparing to write my BA on James Joyce's Ulysses. I'm extremely versatile with expertise in a range of subjects and really enjoy helping students learn. I also excel at standardized testing and am excited to share the strategies that made me successful. In my free time, some of my favorite things to do are act, play guitar, and bike along the river. Hobbies: reading, writing, art, books, music
Pallavi
Pre-Algebra Tutor • +107 Subjects
I am a graduate of The University of Pennsylvania in Philadelphia. I received a Bachelor of Arts in Biology (Neurobiology concentration), a Bachelor of Science in Economics (Healthcare Management and Policy concentration), and a Master's in Biology. Throughout my undergraduate, I have loved tutoring college and high school students in Math, English, Physics, and Biology. I have also volunteered as an ESL instructor. As a medical school applicant, I have taken numerous standardized tests, and I love helping students figure out strategies that work best for their learning! In my spare time, I enjoy teaching kickboxing, dancing, and baking.
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Calculus Tutor • +56 Subjects
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Selamawit
Pre-Algebra Tutor • +21 Subjects
I am a University of Pennsylvania educated tutor with expert knowledge in pre-medical courses and public health statistical analysis. I have three years of experience in bench genetics and clinical research. I really enjoy sciences and writing and have good eye for essay editing. I have taken each and every one of the tests and classes I tutor in repeatedly so my tutoring sessions will also include effective study strategy tips. When I am not nerding out, I enjoy Netflix shows and salsa dancing. I would be happy to provide you with any academic help you need.
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Pre-Algebra Tutor • +43 Subjects
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Calculus Tutor • +38 Subjects
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Ted
Pre-Algebra Tutor • +22 Subjects
I am an M.D. from Houston with an excellent academic background and a passion for teaching. I am affable, reliable, and I go the extra mile for each and every student.
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Frequently Asked Questions
Students often find hypothesis testing and p-value interpretation challenging—many memorize the mechanics without understanding what they're actually testing or why a p-value isn't the probability their hypothesis is true. Survival analysis and time-to-event data also trip up students because they require thinking about censoring and risk sets differently than standard statistical methods. Additionally, the transition from basic probability to applied distributions (binomial, normal, Poisson) in a biological context confuses students who haven't connected the math to real research scenarios like disease prevalence or drug efficacy trials.
Expert tutors connect abstract formulas to real biomedical research—for example, explaining why the standard error matters by showing how it relates to confidence intervals in a clinical trial context, rather than just deriving it algebraically. They help students practice interpreting output from statistical software (R, SAS, SPSS) by asking questions like 'What does this confidence interval tell us about the treatment effect?' rather than 'How do you calculate it?' This approach builds conceptual understanding by anchoring statistics to the biological questions researchers actually ask.
Regression in Biostatistics involves not just fitting lines but interpreting coefficients in context—understanding that a log-odds ratio in logistic regression isn't intuitive, or that confounding and interaction terms require thinking about causal relationships, not just correlation. Students also struggle with model assumptions (linearity, homoscedasticity, independence) because they're used to seeing these as checkbox items rather than conditions that affect whether their conclusions about patient outcomes or disease mechanisms are valid. Tutors help by working through real datasets where violations of assumptions actually matter to interpretation.
Many Biostatistics word problems hide the statistical question in clinical or epidemiological language—a student might read 'Does this drug reduce mortality?' but not recognize it as a hypothesis test problem. Tutors teach students to identify key components: What's the population? What's being measured? Is this about comparing groups, estimating a parameter, or predicting outcomes? By working through problems systematically and asking 'What statistical method answers this question and why?', students develop the pattern recognition to tackle unfamiliar scenarios on exams or in research projects.
Tutors help students use software (R, SAS, or Python) not as a black box but as a tool for understanding—running analyses, interpreting output, and checking assumptions. For example, a tutor might have a student generate a Q-Q plot to visually assess normality, then discuss what violations mean for their inference about treatment effects. This hands-on approach prevents the common mistake of running analyses without understanding what assumptions they require or how to validate results, which is critical in biomedical research where incorrect conclusions affect real patients.
Probability is foundational—students who struggle with conditional probability, Bayes' theorem, or probability distributions often hit a wall when learning likelihood-based inference or understanding sensitivity and specificity in diagnostic testing. Tutors identify gaps in probability understanding early and reinforce concepts like 'P(disease | positive test) is not the same as P(positive test | disease)' through clinical examples, since Biostatistics students need these concepts to interpret medical tests correctly. Building this foundation prevents students from memorizing formulas without grasping why they work.
Study design (randomized controlled trials, observational studies, cohort designs) directly determines which statistical methods are appropriate and what conclusions can be drawn—but many students treat design as separate from analysis rather than foundational to it. Tutors help students see that confounding in an observational study requires different analytical approaches than a randomized trial, and that the design determines whether you can claim causation. This connection is crucial because misunderstanding design often leads to inappropriate statistical choices and overstated conclusions.
Biostatistics anxiety often stems from feeling like there's one 'right way' to solve a problem or interpret results, when actually the field requires judgment about assumptions, sample size, and practical significance. Tutors reduce anxiety by emphasizing that expert statisticians also check assumptions, run sensitivity analyses, and consult references—it's not about memorizing everything. Working through problems step-by-step, asking 'Why does this method work here?' and 'What could go wrong?', helps students see themselves as problem-solvers rather than formula-appliers, which builds genuine confidence for exams and real research work.
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