Award-Winning Biostatistics Tutors
serving Ann Arbor, MI

Three years as an ESL instructor and a summa cum laude biology degree taught Ruth something most tutors learn the hard way — explaining quantitative concepts clearly matters as much as understanding them. Now in medical school, she breaks down biostatistics topics like study design, sensitivity and specificity, and interpreting p-values by connecting them to the clinical research she encounters daily in her coursework.

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.

Before medical school, Macklin struggled with coursework himself — which means he knows exactly where biostatistics concepts like sensitivity, specificity, and p-value interpretation trip students up, because they once tripped him up too. Now a dean's list third-year med student, he breaks down study design and statistical reasoning by walking through the clinical research papers he's actively reading. 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.

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.

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.

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.

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.

Studying behavioral neuroscience at Lehigh and now pursuing a Master's in Public Health at George Washington, Katherine has lived inside biostatistics — from chi-square tests and logistic regression to survival analysis and epidemiological study design. She teaches the subject the way she learned to use it: tied to real research questions about populations and health outcomes, so formulas stop feeling arbitrary and start making sense.

Matthew's Master's in Educational Measurement and Statistics at USF means he's not just familiar with biostatistics methods — he's studying the theory behind how statistical tests are constructed and validated, which gives him unusual depth when explaining concepts like power analysis, effect sizes, and the assumptions underlying common tests. His psychology background adds a second layer: he learned biostatistics the way most students encounter it, applied to human subjects research with messy behavioral data. Rated 4.8 by students.

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.

Biochemistry and psychology together mean Nathaniel has designed experiments on both the molecular and behavioral sides — two domains where the datasets look completely different but the statistical logic (choosing tests, interpreting p-values, understanding variance) is the same. That dual training makes him particularly good at explaining when to apply specific methods like t-tests, ANOVA, or correlation analysis, because he's had to think through which tool fits which kind of biological question. Rated 5.0 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.

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.

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.