Award-Winning Biostatistics Tutors
serving Detroit, 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.

Bioengineering lab courses at UIC's Honors College don't let you hand-wave through your data — Shouvik has had to choose appropriate statistical tests for biological experiments, interpret p-values from tissue engineering datasets, and justify sample sizes in formal reports. That firsthand experience with real biological variability makes him effective at teaching concepts like hypothesis testing, descriptive statistics, and study design. Rated 4.9 by students.

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.

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.

As a teaching assistant for a graduate-level biostatistics and R programming course at Nova Southeastern University, Tyler has walked students through everything from ANOVA designs to logistic regression in biological contexts. His own master's research on marine fish ecology keeps him actively using these methods — running power analyses, interpreting p-values, and building statistical models from messy field data.

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.

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.

Medical school trains you to read studies critically — picking apart odds ratios, questioning sample sizes, and spotting when a confidence interval undermines a paper's bold conclusion. Sanjul, now in his final year of osteopathic medical training with a biology foundation, brings that clinical lens to biostatistics concepts like hypothesis testing, relative risk, and regression modeling. Rated 5.0 by students.

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.

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.

Lindsay's biology degree with a math minor means she's lived on both sides of biostatistics — understanding the experimental design behind biological research and the quantitative tools needed to draw valid conclusions from it. She digs into concepts like probability distributions, hypothesis testing, and regression by connecting each method to the kind of data a biologist would actually collect. Rated 4.9 by students.

Running genomics experiments at Washington University and now studying synapse formation at Duke, Kristina has spent years generating the kind of high-dimensional biological data where statistical missteps — wrong multiple comparisons corrections, underpowered sample designs — can sink a paper before peer review. She teaches biostatistics concepts like ANOVA, regression, and experimental power by drawing on the actual analytical decisions she's made with her own neural datasets. Rated 5.0 by students.

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.