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.

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.

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.

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.

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.

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.

Most biostatistics struggles come down to not knowing which test to use or why — is this a chi-square situation or a t-test, and what does the p-value actually mean? Amanda's Master of Public Health training required heavy coursework in epidemiological statistics, so she teaches biostatistics with the kind of applied, research-oriented framing that makes concepts like confidence intervals and regression analysis click. She walks through real study designs to show how statistical choices shape conclusions.

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.

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.

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.

Courtney's graduate research in aquatic ecology means she's wrestled with the messy, real-world datasets that make biostatistics click — figuring out which test to run when sample sizes are uneven, or whether a correlation in field data actually holds up under regression. That experience analyzing ecological patterns, combined with her MS in Biology, grounds her teaching of concepts like experimental design, ANOVA, and data interpretation in the biological questions that give the numbers meaning. Rated 5.0 by students.