Award-Winning Biostatistics Tutors
serving Bonita Springs, FL

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.

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.

Between her biology degree, coursework in statistics and quantitative reasoning, and subjects like bioinformatics and nutrition, Michelle has spent years working at the intersection of biological data and statistical analysis — exactly where biostatistics lives. She breaks down concepts like study design, probability distributions, and measures of association by rooting them in the biological questions that make those methods necessary in the first place. Rated 4.9 by students.

I am a law student, but I took an unusual route to get there. I used to attend medical school but had a change of heart in my career path. Part of this was due to my political science major (double major with biology) in college as well as a number of Spanish and other courses that I took. Tutoring is something, I feel, that has come naturally to me, even back to my high school days. My goal is to help you learn as much as you can and reach your true potential. I will work hard to make sure that this happens, as long as you put in the work, too! We will work together to tailor your learning experience to your needs.

Running statistical analyses on genetic and ecological data has been a core part of Nicholas's graduate research, which means biostatistics isn't abstract for him — it's how he answers real scientific questions. He teaches concepts like logistic regression, survival analysis, and experimental design by grounding them in biological datasets students can actually interpret. Rated 4.8 by students.

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.

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.

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.

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.

Sima's Master of Science in Epidemiology with a concentration in Biostatistics means she doesn't just teach survival analysis, regression modeling, and study design — she uses them daily in her cancer research at Mount Sinai. She walks students through everything from interpreting p-values to building multivariate models, grounding each concept in real epidemiological datasets.

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.

Statistical thinking doesn't come naturally to most biology students, which is exactly where biostatistics courses lose people. Mike tackles concepts like odds ratios, survival analysis, and hypothesis testing by grounding each method in the clinical research context where it actually gets used — an approach shaped by his medical training at Rutgers.

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.

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.

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.

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.

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.