Award-Winning Biostatistics Tutors
serving Gainesville, 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.

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.

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.

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.

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.

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.

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.

Epidemiology graduate training is essentially applied biostatistics — Naushaba spent her master's program designing studies, calculating relative risks, and interpreting the kinds of population-level data that make concepts like confounding, bias, and survival analysis concrete rather than abstract. Her chemistry background adds a quantitative backbone that keeps her comfortable with the math underlying methods like logistic regression and chi-square tests.

Studying neuroscience on a pre-med track, Jessica encounters biostatistics constantly — from interpreting p-values in research papers to running regression analyses on experimental data. She teaches concepts like hypothesis testing, confidence intervals, and study design by grounding them in actual biological questions rather than abstract formulas.

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.

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.

Kimanthi's biomedical engineering training at Duke meant designing experiments that demanded careful statistical thinking — selecting appropriate tests, calculating power, and interpreting results from biological datasets. Now in medical school, she teaches biostatistics concepts like hypothesis testing and regression by grounding them in the clinical and laboratory scenarios where they actually come up. Rated 5.0 by students.