Award-Winning Biostatistics Tutors
serving Cape Coral, 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.

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.

Graduate-level ecology research forced Scott to live inside statistical software — running ANOVAs, building regression models, interpreting p-values from messy field data. He approaches biostatistics by pairing each test (chi-square, t-test, logistic regression) with the biological question it actually answers, which makes choosing the right analysis feel intuitive instead of arbitrary.

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.

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.

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.

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.

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.

Graduate coursework in Health Services Administration means Alan regularly encounters the statistical side of healthcare — interpreting study outcomes, evaluating public health data, and understanding how measures like relative risk and confidence intervals shape administrative decisions. His biology degree gives him the scientific literacy to contextualize the datasets, while his 5.0 rating speaks to how clearly he breaks down concepts like hypothesis testing and descriptive statistics for students who find the quantitative side intimidating.

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.

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.

Ingrid's biomedical engineering coursework at Northwestern — including undergraduate research in the John Rogers Lab — gave her hands-on experience designing experiments and interpreting the statistical methods that underpin clinical and biological research. She breaks down concepts like survival analysis, logistic regression, and confidence intervals by tying them to real biomedical datasets rather than abstract formulas.

Having earned a PhD in Statistics, Sam digs into biostatistics with the depth that graduate and pre-med students actually need — survival analysis, logistic regression, study design, and interpreting odds ratios in clinical contexts. His undergraduate training in biomedical engineering gives him a native fluency with the biological applications that make this field distinct from general stats.