Award-Winning Biostatistics Tutors
serving Yonkers, NY

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.

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.

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

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.

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.

Graduate-level biology research in evolution and bioanthropology means Alex regularly works with the kinds of datasets where understanding variance, choosing appropriate statistical tests, and interpreting p-values isn't abstract — it's how you defend your findings. His master's training, combined with a broad undergraduate foundation in both biology and English, gives him a knack for translating dense statistical concepts like probability distributions and measures of association into language that actually makes sense. Rated 4.8 by students.

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 to biostatistics concepts like hypothesis testing, relative risk, and regression modeling. 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.

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.

Studying biology at Duke while conducting field research on Hawaiian monk seals meant Emma had to grapple with real ecological datasets — the kind where choosing between a t-test and a Mann-Whitney U actually changes your conclusions. That hands-on experience with biological data analysis, paired with her 4.9 rating from students, makes her especially effective at teaching the statistical reasoning behind study design and data interpretation.

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 background means she teaches biostatistics concepts like regression and hypothesis testing through the lens of someone who's actually had to defend her statistical choices in lab reports and research. Rated 4.9 by students.

Biochemistry lab work at the University of Michigan meant Kirby couldn't just run experiments — she had to decide whether her results actually meant something, which meant grappling with statistical tests, error analysis, and sample size considerations before any conclusion could hold up. That training in evaluating biological data carries directly into teaching biostatistics concepts like p-values, variance, and choosing the right analytical approach for a given dataset. Rated 4.9 by students.

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.

I am also interested in tutoring college students preparing for the GRE general test. For test preparation, I assign a decent amount of homework each week and I spend the majority of my sessions going over the questions my students answer incorrectly.

Lindsay's biology degree with a math minor means she's lived on both sides of biostatistics — understanding the experimental design behind biological research and the quantitative tools needed to draw valid conclusions from it. She digs into concepts like probability distributions, hypothesis testing, and regression by connecting each method to the kind of data a biologist would actually collect. Rated 4.9 by students.

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 why a particular statistical method fits a biological question, whether students are wrestling with relative risk calculations or figuring out when to use a t-test versus ANOVA.