Award-Winning Biostatistics Tutors
serving Jacksonville, FL

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.