Award-Winning Biostatistics Tutors
serving Boston, MA

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.

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.

Group study sessions in her Master of Science program at BU's medical school often turned into impromptu teaching sessions where Sydney had to translate dense statistical concepts — p-values, confidence intervals, measures of central tendency — into language that clicked for classmates approaching the material from different angles. Her neuroscience background means she's worked with the kinds of biological datasets where understanding variability and choosing appropriate tests isn't abstract but essential to interpreting research results. Rated 5.0 by students.

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.

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.

During her time at Northeastern, Sarah tutored peers in biostatistics, walking them through probability distributions, hypothesis testing, and regression analysis in the context of real biological data sets. She knows where students typically get stuck — translating a research question into the right statistical test — and tackles that gap head-on.

Irene's PhD in Mathematics and Computer Science means she approaches biostatistics from the pure quantitative side — probability theory, distributions, and the mathematical proofs underlying tests like ANOVA and chi-square that most biology-focused instructors skip over. For students who struggle less with the clinical context and more with the math itself, that depth is exactly what closes the gap. Rated 4.9 by students.

Neurobiology research at UT Austin meant Hiral couldn't just run experiments — she had to make sense of the data coming out of them, from deciding whether a t-test or ANOVA fit her experimental design to interpreting p-values that determined whether her results meant anything at all. That direct experience wrangling biological datasets gives her a practical grip on concepts like probability, measures of central tendency, and hypothesis testing. Rated 5.0 by students.

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.

Biology coursework generates the kind of data — population counts, gene expression levels, epidemiological surveys — where understanding which statistical test to run matters as much as understanding the biology itself. Ade's biology degree means he teaches concepts like probability distributions, measures of central tendency, and hypothesis testing by starting from the biological question rather than the formula sheet, so the reasoning behind each method clicks before the calculations begin.

Sitting at the intersection of biology and statistics, biostatistics trips up students who are strong in one field but shaky in the other. Pallavi's dual background — a master's in biology plus a BS in economics with a policy concentration — means she's fluent in both experimental design and the quantitative methods (regression, survival analysis, hypothesis testing) that make sense of biological data.

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.

Casey's bioengineering degree required designing and analyzing experiments where statistical choices — picking the right test for biological variability, interpreting p-values from cell culture data, calculating sample sizes for meaningful results — were baked into every project. That training means she teaches concepts like probability distributions, hypothesis testing, and regression by tying them to the kinds of biological datasets students will actually encounter in research and clinical coursework.

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.

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

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.

Alex's master's thesis at Rice centered on environmental statistics, giving him direct experience with the tools that define biostatistics: survival analysis, logistic regression, study design, and interpreting odds ratios in research contexts. He unpacks statistical software output and walks through the logic behind each test so students understand both the math and the methodology.

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.