Award-Winning Biostatistics Tutors
serving Long Beach, CA

A physics and mathematics degree means Sarah spent years working with error analysis, probability distributions, and the kind of rigorous data interpretation that underpins biostatistics — skills she built not from a textbook overview but from labs and problem sets where getting the statistics wrong meant getting the physics wrong. She approaches topics like measures of central tendency, variance, and hypothesis testing with the mathematical precision her training demands, breaking down formulas into the logic behind them.

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.

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.

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.

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.

Analyzing shark movement data across ocean receiver arrays means Sofia has spent years wrangling real ecological datasets — running survival analyses, building generalized linear mixed models, and interpreting spatial statistics. Her PhD research required designing studies with messy field data, so she teaches biostatistics through the lens of practical problems like sample size limitations, repeated measures, and accounting for detection probability.

Between her biology major, math minor, and four years of medical school coursework in community health and preventive medicine, Emily has encountered biostatistics from every angle — interpreting clinical studies, running analyses on biological datasets, and applying concepts like sensitivity, specificity, and measures of association in evidence-based medicine. She teaches the logic behind choosing statistical methods, connecting each test back to the clinical or research scenario that makes it meaningful. Rated 5.0 by students.

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.

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.

Studying biology at Duke while conducting field research on Hawaiian monk seals meant Emma had to grapple with real ecological datasets — the kind where choosing between a t-test and a Mann-Whitney U actually changes your conclusions. That hands-on experience with biological data analysis, paired with her 4.9 rating from students, makes her especially effective at teaching the statistical reasoning behind study design and data interpretation.

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.

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.

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.