Award-Winning Biostatistics Tutors
serving Manhattan, NY

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 biostatistics department and brings that classroom-tested ability to unpack dense material into clear, structured explanations. If you're wrestling with SAS output or trying to interpret an odds ratio for a thesis, she's been there recently.

Wall Street research demands a kind of statistical fluency that translates surprisingly well to biostatistics — Frank spent years dissecting datasets, evaluating risk models, and stress-testing assumptions before pivoting to teaching statistics at both the AP and college level. He breaks down concepts like hypothesis testing, probability distributions, and regression by emphasizing the logic behind choosing a method, drawing on the same analytical rigor he applied to financial research. His MBA and quantitative background give him a practical, numbers-first approach that cuts through the abstraction many students struggle with.

Graduate coursework in Health Services Administration means Alan regularly encounters the statistical side of healthcare — interpreting study outcomes, evaluating public health data, and understanding how measures like relative risk and confidence intervals shape administrative decisions. His biology degree gives him the scientific literacy to contextualize the datasets, while his 5.0 rating speaks to how clearly he breaks down concepts like hypothesis testing and descriptive statistics for students who find the quantitative side intimidating.

I am and have always been committed to education and helping students in any way I can to achieve their academic goals.

Sima's Master of Science in Epidemiology with a concentration in Biostatistics means she doesn't just teach survival analysis, regression modeling, and study design — she uses them daily in her cancer research at Mount Sinai. She walks students through everything from interpreting p-values to building multivariate models, grounding each concept in real epidemiological datasets.

Three years teaching high school biology in New Jersey meant Sasha was constantly designing assessments and interpreting student performance data — skills that map directly onto biostatistics concepts like distributions, variability, and drawing valid inferences from samples. Her master's in science education and undergraduate biology training give her a dual grasp of both the life science context behind biological datasets and the statistical reasoning needed to analyze them. She breaks down topics like chi-square tests and descriptive statistics by rooting them in the kinds of biological questions that make the math feel purposeful.

Running a program that teaches elementary school girls math and science means Kara regularly translates quantitative concepts for audiences who need clarity over jargon — a skill that carries directly into breaking down biostatistics topics like descriptive measures, basic probability, and interpreting study results. Her biology coursework on the pre-med track at UVA, combined with her public health studies, gives her familiarity with the kinds of epidemiological data and research designs that biostatistics problems are built around.

Currently pursuing a graduate degree in statistics while holding a sociology background, Evan knows how to bridge the gap between raw quantitative methods and the population-level questions that drive biostatistics — things like interpreting odds ratios, building regression models, or deciding when a nonparametric test makes more sense than a parametric one. His sociology training means he's worked with survey data and demographic datasets where sloppy statistical reasoning leads to misleading conclusions. Rated 5.0 by students.

Teaching AP Chemistry, Honors Biology, and Anatomy & Physiology every day means Samuel is constantly working with the kinds of experimental data that biostatistics is built to analyze — comparing treatment groups, interpreting variability in lab results, and evaluating whether observed differences are real or just noise. His master's in science education sharpens how he breaks down concepts like descriptive statistics, probability, and hypothesis testing so they feel like practical tools rather than abstract formulas. Rated 4.9 by students.

Biochemistry and molecular biology coursework means Aditya is constantly reading papers packed with survival curves, p-values, and confidence intervals — the kind of statistical literacy that comes from needing it to understand his own field, not just passing an exam. He breaks down concepts like probability distributions and measures of central tendency by walking through the biological data they're actually applied to, so the formulas feel purposeful rather than abstract.

Graduate coursework in global health and population at Harvard means Sarah's biostatistics training is rooted in real epidemiological research — calculating measures of association, building regression models, and interpreting results in the context of population-level health questions. Her undergraduate biology degree adds fluency with the scientific reasoning behind study design, so she can unpack why a particular statistical test fits a given dataset rather than just drilling the formula. Rated 5.0 by students.

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 the kind of applied, research-oriented framing that makes concepts like confidence intervals and regression analysis click. She walks through real study designs to show how statistical choices shape conclusions.

Statistical thinking doesn't come naturally to most biology students, which is exactly where biostatistics courses lose people. Mike tackles concepts like odds ratios, survival analysis, and hypothesis testing by grounding each method in the clinical research context where it actually gets used — an approach shaped by his medical training at Rutgers.

Graduate-level ecology research forced Scott to live inside statistical software — running ANOVAs, building regression models, interpreting p-values from messy field data. He approaches biostatistics by pairing each test (chi-square, t-test, logistic regression) with the biological question it actually answers, which makes choosing the right analysis feel intuitive instead of arbitrary.

Dan's graduate work in plant biology meant running his own statistical analyses on biological datasets — the kind where understanding ANOVA designs, choosing appropriate post-hoc tests, and interpreting interaction effects isn't theoretical but determines whether your thesis holds up. That research experience, combined with a 5.0 rating from students, grounds his biostatistics teaching in the practical decisions researchers actually face when moving from raw data to publishable results.

An applied mathematics degree paired with doctoral-level engineering work means Professor Florence has spent years building and defending statistical models — the exact skill set biostatistics demands when students face questions about study design, probability distributions, or interpreting regression output. Her psychology background at UCLA also exposed her to the behavioral research side, where concepts like ANOVA, effect size, and sampling methodology show up in every published study. Rated 4.6 by students.

Running experiments in a Harvard Medical School lab means Patrick doesn't just teach biostatistics concepts — he applies them to real cellular and molecular datasets, from choosing the right statistical test for gene expression data to interpreting p-values in preclinical studies. His PhD in Cellular and Molecular Biology built the quantitative backbone for designing experiments with proper controls, power calculations, and regression models. Rated 5.0 by students.

Biochemistry and psychology together mean Nathaniel has designed experiments on both the molecular and behavioral sides — two domains where the datasets look completely different but the statistical logic (choosing tests, interpreting p-values, understanding variance) is the same. That dual training makes him particularly good at explaining when to apply specific methods like t-tests, ANOVA, or correlation analysis, because he's had to think through which tool fits which kind of biological question. Rated 5.0 by students.