Award-Winning Biostatistics Tutors
serving Queens, 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.

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.

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.

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.

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.

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.

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.

Psychology research lives and dies by statistics — every study Katelyn encountered during her degree required interpreting effect sizes, understanding when to apply a chi-square test, and evaluating whether a sample actually supports a paper's claims. That training in research methods translates directly to biostatistics concepts like probability, measures of central tendency, and hypothesis testing, especially for students who need the statistical logic explained through behavioral and health science examples rather than pure math.

Having earned a PhD in Statistics, Sam digs into biostatistics with the depth that graduate and pre-med students actually need — survival analysis, logistic regression, study design, and interpreting odds ratios in clinical contexts. His undergraduate training in biomedical engineering gives him a native fluency with the biological applications that make this field distinct from general stats.

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.

Reading medical literature requires fluency in biostatistics — knowing when a study's design actually supports its conclusions and when a low p-value is misleading. Ted lived this throughout his medical education, interpreting survival curves, odds ratios, and multivariate analyses in clinical contexts. He teaches biostatistics as a critical-thinking toolkit, connecting each method to the research questions it's designed to answer.

A PhD in Statistics paired with a mathematics foundation means Bahaeddine doesn't just teach biostatistics formulas — he can explain the probabilistic theory underneath methods like logistic regression, survival analysis, and hypothesis testing, then show how those methods apply to biological and clinical datasets. Fifteen years of teaching statistics at the college level have given him a deep catalog of examples for making abstract concepts like p-values, confidence intervals, and study design feel concrete and intuitive.

Gabriel has taught biostatistics at the undergraduate level, walking students through hypothesis testing, regression analysis, and experimental design with real biological datasets. His computational neuroscience research adds a practical dimension — he designs and analyzes electrophysiological experiments, so concepts like p-values and confidence intervals aren't abstract formulas but tools he uses weekly.