Award-Winning Biostatistics Tutors
serving Grand Rapids, 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.

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.

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.

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.

Karista's PhD in Environmental Science meant analyzing messy field data — the kind where uneven sample sizes, non-normal distributions, and confounding variables force you to think carefully about which statistical approach actually fits. That experience designing and defending her own analyses carries over directly when she teaches concepts like ANOVA, multivariate regression, and experimental design in biostatistics. Rated 5.0 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.

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.

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.

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.

Earning her Master of Science in Global Health from Duke meant Andria lived inside biostatistics — designing studies, running regression analyses, and interpreting p-values in the context of real epidemiological data. She unpacks concepts like confidence intervals, odds ratios, and survival analysis by grounding them in the public health questions they're built to answer.

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.

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.

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.

Elliot's PhD in neuroscience meant wrestling with the kinds of biological datasets where choosing the wrong statistical test can invalidate years of research — from analyzing neural firing rates with repeated-measures designs to modeling dose-response curves with logistic regression. That firsthand experience with experimental data makes him especially sharp at teaching concepts like power analysis, ANOVA, and survival analysis, because he's had to defend those choices in his own published work. Rated 5.0 by students.

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.