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.

Courage's training sits at an unusual intersection — dual bachelor's degrees in computer science and biological/physical sciences, plus a master's in environmental science — which means he's worked with biological datasets from both the programming and the scientific sides. That crossover makes him especially sharp at teaching concepts like data distributions, hypothesis testing, and regression analysis using tools like Python and SQL, bridging the gap between understanding the biology behind a study and actually crunching the numbers.

Preparing for medical school means Serena has worked through the full gauntlet of premed coursework — and biostatistics sits right at the intersection of her biology degree and the quantitative reasoning she's built through years of tutoring math from algebra through calculus. She breaks down concepts like study design, measures of central tendency, and statistical significance by keeping the biological question front and center, so the formulas serve the science rather than the other way around. Holds a 5.0 rating from students.

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.

Studying neuroscience on a pre-med track, Jessica encounters biostatistics constantly — from interpreting p-values in research papers to running regression analyses on experimental data. She teaches concepts like hypothesis testing, confidence intervals, and study design by grounding them in actual biological questions rather than abstract formulas.

Three years of bench genetics and clinical research gave Selamawit hands-on experience designing studies, running statistical tests, and interpreting p-values in contexts where the results actually mattered. She brings that practical fluency to biostatistics topics like regression analysis, survival curves, and hypothesis testing. Her University of Pennsylvania public health training means she knows exactly how these methods apply to epidemiological and clinical data.

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.

Between a neuroscience bachelor's, a biotechnology master's, and current medical training, Rithi has run into biostatistics from every angle — analyzing neural data in research, evaluating clinical study designs, and interpreting the kind of messy biological datasets where a wrong assumption about normality can derail an entire analysis. She breaks down concepts like survival curves, relative risk calculations, and test selection by walking through the actual research scenarios that make each method necessary. Rated 4.9 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.

As a biomedical engineering undergrad at Rice, Aurnab regularly works with biological datasets where statistical decisions — selecting the right test for variable sample sizes, interpreting p-values from experimental results — directly shape whether a project's conclusions hold up. That engineering-driven statistical thinking translates naturally to teaching concepts like probability distributions, hypothesis testing, and confidence intervals in a biostatistics context. Rated 4.9 by students.

Biomedical engineering coursework generates a constant stream of statistical problems — analyzing device performance data, interpreting clinical trial outcomes, modeling biological variability. Khushal tackles biostatistics from that engineering angle, breaking down hypothesis testing, probability distributions, and regression by connecting each method to the quantitative demands of his own BME program. Rated 5.0 by students.

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.

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 analysis, logistic regression, and confidence intervals by tying them to real biomedical datasets rather than abstract formulas.