Award-Winning Biostatistics Tutors
serving New York, 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.

Biostatistics sits right at the intersection of Michael's two strengths — biology and quantitative analysis. His master's research required designing experiments, selecting appropriate statistical models, and interpreting output from tools like R, so he walks students through survival analysis, logistic regression, and study design with the confidence of someone who's applied each method to real data.

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.

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.

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.

Epidemiology graduate training is essentially applied biostatistics — Naushaba spent her master's program designing studies, calculating relative risks, and interpreting the kinds of population-level data that make concepts like confounding, bias, and survival analysis concrete rather than abstract. Her chemistry background adds a quantitative backbone that keeps her comfortable with the math underlying methods like logistic regression and chi-square tests.

Kimanthi's biomedical engineering training at Duke meant designing experiments that demanded careful statistical thinking — selecting appropriate tests, calculating power, and interpreting results from biological datasets. Now in medical school, she teaches biostatistics concepts like hypothesis testing and regression by grounding them in the clinical and laboratory scenarios where they actually come up. Rated 5.0 by students.

Studying behavioral neuroscience at Lehigh and now pursuing a Master's in Public Health at George Washington, Katherine has lived inside biostatistics — from chi-square tests and logistic regression to survival analysis and epidemiological study design. She teaches the subject the way she learned to use it: tied to real research questions about populations and health outcomes, so formulas stop feeling arbitrary and start making sense.

As a first-year med student at Pitt with a biology degree from Tufts, Danielle is encountering biostatistics exactly where it becomes unavoidable — interpreting clinical study results, calculating sensitivity and specificity, and evaluating whether a paper's p-value actually means what the authors claim. Her 36 ACT composite reflects the kind of precise analytical thinking she brings to breaking down concepts like confidence intervals, relative risk, and study design for students who find the statistical side of biology intimidating. Rated 5.0 by students.

Rachel's Master's in Environmental Health Sciences from Johns Hopkins required the same core biostatistics training that public health students dread — survival analysis, logistic regression, and interpreting epidemiological study results with real population data. Years of conservation fieldwork since then have kept her close to the kind of messy environmental datasets where picking the right statistical test actually shapes policy decisions. She connects methods like chi-square tests and confidence intervals back to the health and ecological questions they were built to answer.

Between a biochemistry degree and medical school, Effie has encountered biostatistics from both directions — designing experiments that require careful statistical reasoning and then reading clinical literature where flawed methodology can derail a study's conclusions. She breaks down concepts like sensitivity and specificity, confidence intervals, and study design by connecting them to the research papers and clinical scenarios she's actively working through in her medical training.

Ritu's biology degree means she's spent time with the kinds of datasets where understanding variance, sample distributions, and test selection actually determines whether a research conclusion holds up. She teaches concepts like measures of central tendency, probability, and chi-square analysis by walking through the biological scenarios that make each method necessary — not just the formulas. Rated 5.0 by students.

A biology and history double major with a 1580 SAT might seem like an unusual biostatistics tutor, but Rachel's biology training means she's worked through the core statistical methods — descriptive statistics, probability, and study design — that underpin biological research. She breaks down concepts like p-values and confidence intervals by connecting them to the biological experiments that generate the data in the first place.