Award-Winning Biostatistics Tutors
serving North Port, 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.

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.

Psychology research lives and dies by statistics — every study Katelyn encountered during her degree required interpreting effect sizes, understanding when to apply a chi-square test, and evaluating whether a sample actually supports a paper's claims. That training in research methods translates directly to biostatistics concepts like probability, measures of central tendency, and hypothesis testing, especially for students who need the statistical logic explained through behavioral and health science examples rather than pure math.

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.

Engineering coursework at MIT forced Natasha to build statistical models from biological and chemical datasets — the kind where understanding variance, distributions, and experimental design isn't optional but essential to getting meaningful results. Her chemical and biomolecular engineering background means she teaches biostatistics concepts like regression and hypothesis testing through the lens of someone who's actually had to defend her statistical choices in lab reports and research. 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.

Courage's training sits at an unusual intersection — dual bachelor's degrees in computer science and biological/physical sciences, plus a master's in environmental science — which means he's worked with biological datasets from both the programming and the scientific sides. That crossover makes him especially sharp at teaching concepts like data distributions, hypothesis testing, and regression analysis using tools like Python and SQL, bridging the gap between understanding the biology behind a study and actually crunching the numbers.

Biochemistry majors don't just memorize pathways — they learn to read primary literature, which means grappling with p-values, confidence intervals, and study designs long before a formal biostatistics course. Hunter's biochemistry training at Boston College, followed by a Master's in Biomedical Sciences at Tufts, gave him repeated practice interpreting the statistical methods embedded in research papers. Rated 5.0 by students, he's now headed to Duke for a PhD, where that statistical literacy only deepens.

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.

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.

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.

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.

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.