Award-Winning Statistics Tutors
serving Mobile, AL

I graduated with a BA in Politics and a BA in Anthropology from NYU in 2022. I also hold a MS in Biomedical Anthropology and a Graduate Certificate in Forensic Health and Nursing from SUNY Binghamton (graduated May 2024). I have worked in education, with a previous position with America Reads/America Counts teaching elementary students in the NYC public school system. My previous virtual tutoring experience was with Curious Cardinals, where I had tutored high school students with the following: forensic science, biology (anatomy, genetics), SAT Evidence-Based Reading and Writing, ACT English and ACT Reading. I have experience working in forensic science policy at the Innocence Project, which is an organization focused on exonerating individuals who were wrongfully convicted of crime. In this role, I have written testimonials, case reports, and research articles on forensic identification methods and their legal, ethical, political, and privacy implications in casework. I have also worked in the forensic science sector as a Criminalist II at the NYC Office of Chief Medical Examiner (OCME). In this position, I assisted medical examiners (forensic pathologists) in the positive identification of deceased individuals throughout New York City. I also assisted in missing persons inquiries that were brought to the agency. Recently, I completed a research project in Vitoria de Santo Antao and Recife (both in the state of Pernambuco, in northeast Brazil) as a Fulbright Grantee. My research focused on osteology, forensic anthropology, and forensic genetics. My project involved metric analyses of bones combined with analyzing genetic information from DNA taken from bones. Currently, I am a doctoral student of Biological Sciences - Genetics at the University of Sao Paulo (USP). As a doctoral student, my dissertation project is expanding on the research I did with the Fulbright grant and is based on the central question/problem: What is the evolutionary relationship between skeletal morphology (primarily size), external pigmentation (i.e., skin, hair), and the presence of vitamin D deficiencies (which are indicated by the presence of skeletal lesions and decreased bone mineral density)? My favorite subjects in school were biology and anatomy, U.S. history, statistics, pre-algebra and algebra I, language and literature. I also partook in concert band and orchestra both in high school and in college. I am excited to share my skills and knowledge in these subjects with you, as well!

I am an aerospace engineering student. I am very passionate about teaching math, physics, and chemistry. I realized tutoring was my calling while working as a tutor at my school. Seeing students gain a greater understanding of the subject was the highlight of my day while working there.

Probability distributions, hypothesis testing, and confidence intervals make a lot more sense when you've actually used them to analyze real data. Emma applied statistical methods throughout her biology research at Duke — including fieldwork on Hawaiian monk seals — so she teaches stats as a practical tool rather than an abstract formula sheet. Rated 4.9 by students.

Probability distributions, hypothesis testing, and regression analysis are central to both engineering and business — and Caroline has graduate-level training in both. Her mechanical engineering M.S. from WashU built her statistical modeling skills, while her current MBA at MIT Sloan sharpens how she interprets data for real-world decisions. She teaches the reasoning behind each method so formulas stop feeling like black boxes.

Studying conducting at Juilliard means Molly lives in data — analyzing scores, interpreting patterns, and making decisions based on complex information. She brings that same analytical mindset to statistics, breaking down probability distributions, hypothesis testing, and data interpretation into logical steps. Her 5.0 client rating speaks to how clearly she communicates even the trickiest concepts.

The hardest part of statistics for most students isn't the math — it's interpreting what a p-value or confidence interval actually means in context. Vy's training in cognitive studies at Vanderbilt, which is heavily research-methods driven, means she's spent real time designing studies and running analyses. She unpacks concepts like distributions, hypothesis testing, and regression by tying them to concrete research questions.

Kathy's economics degree from Duke meant living inside datasets — regression analysis, probability distributions, hypothesis testing, and statistical inference were daily tools, not abstract concepts. She breaks down problems by connecting the math to what the numbers actually represent, which makes interpreting results feel intuitive rather than formulaic.

Yi's graduate training in research and experimental psychology required heavy use of statistical methods — from hypothesis testing and ANOVA to regression modeling and interpreting p-values in published studies. That hands-on experience with real data analysis means she teaches statistics as a tool for answering questions, not just a set of formulas to memorize.

Understanding when to use a standard deviation versus an interquartile range — or why a skewed distribution changes your entire analysis — requires more than formula memorization. Vinson approaches statistics through data interpretation first, walking through probability distributions, hypothesis testing, and regression with an emphasis on what the numbers actually tell you. He's studying computational math at Rice, where statistical reasoning is woven into his daily coursework.

Most students can plug numbers into a standard deviation formula — the harder part is interpreting what the result actually means in context. Joshitha approaches statistics by connecting every calculation to real-world reasoning: why a confidence interval narrows, what a p-value does and doesn't tell you. Her engineering background at Johns Hopkins means she uses statistical thinking constantly and can show students where these ideas live outside the textbook.

Probability distributions, hypothesis testing, and regression can feel like a foreign language the first time through. Nina breaks these concepts down by connecting them to real datasets and research questions drawn from her biostatistics training at Columbia and NYU. Rated 5.0 by students, she's especially effective at making the jump from formulas to interpretation feel intuitive.

Working as a research assistant in Yale's cognitive neuroscience lab meant Emily ran statistical analyses regularly — hypothesis testing, probability distributions, and interpreting p-values were part of her daily routine. That hands-on experience makes her especially effective at explaining why a statistical method works, not just how to execute it on a calculator.

Studying economics at the undergraduate level means living inside probability distributions, hypothesis tests, and regression models — so Laura treats statistics as a language she already speaks fluently. She breaks down concepts like p-values and confidence intervals by tying them to concrete decision-making scenarios rather than abstract formulas. Her 5.0 rating speaks to how clearly that approach translates for students.

Jonathan holds an MS in Statistics, which means probability distributions, hypothesis testing, and regression analysis aren't just textbook topics for him — they're the core of his graduate training. He breaks down intimidating formulas like Bayes' theorem or ANOVA tables by connecting them to the real-world questions they were designed to answer.