Award-Winning Statistics Tutors
serving Montgomery, 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.

Between her sociology research in undergrad and her MBA coursework, Krupa has run enough regressions, hypothesis tests, and probability models to know exactly where students get tripped up. She tackles the conceptual side — why you'd choose a t-test over a z-test, what a p-value actually means — so the formulas stop feeling arbitrary. Her 4.9 rating speaks to how clearly she communicates these ideas.

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.

Probability distributions, hypothesis testing, confidence intervals — statistics asks students to think in a fundamentally different way than most math courses. Elliot spent years running statistical analyses on neural data during his PhD research, which means he can show exactly how concepts like p-values and regression actually function in practice, not just on a formula sheet.

Understanding statistics means learning to ask the right questions about data before running any test: Is the sample random? What's the shape of the distribution? Could this result have happened by chance? Ethan's policy background gave him years of practice interrogating datasets and translating statistical output into plain-language conclusions, a skill he now brings directly to topics like hypothesis testing and regression analysis.

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.

A PhD statistician who also holds a biomedical engineering degree, Sam teaches introductory and intermediate statistics with an unusual amount of real-world context. Whether the topic is hypothesis testing, confidence intervals, or regression, he unpacks the logic behind each method so students can interpret results critically, not just run calculations.

Kaylah's graduate work in Computational Social Science at the University of Chicago is built almost entirely on statistical methods — probability distributions, hypothesis testing, regression modeling, and data interpretation. She teaches statistics the way she actually uses it: starting with what question you're trying to answer, then selecting and applying the right tool. Her background in cognitive neuroscience research means every example she pulls from is grounded in real data.

A Quantitative Methods minor at Vanderbilt means Heather spent semesters immersed in regression analysis, hypothesis testing, and probability — the exact material that trips up most statistics students. She teaches the reasoning behind each test so students can choose the right method on their own, not just follow a decision flowchart. Her 4.9 rating speaks to that approach.

An economics degree means Maggie didn't just study statistics in a textbook — she applied distributions, hypothesis testing, and regression analysis to real datasets. She teaches students to interpret what a p-value actually tells them and how to choose the right test for a given scenario, building the kind of statistical intuition that carries through exams and research projects alike.

During her psychology degree at Penn, Brittany used statistics constantly — hypothesis testing, probability distributions, regression analysis — as core tools for understanding research. She also tutored middle schoolers in introductory statistics as a volunteer in West Philadelphia, so she's comfortable adjusting her explanations whether someone is learning mean and median or wrestling with p-values.

Most students walk into statistics expecting another math class and get blindsided by the emphasis on interpretation — explaining what a confidence interval actually means, or why correlation isn't causation. Amber tackles that interpretive layer head-on, teaching students to read context before crunching numbers. Her theater background gives her a knack for making abstract concepts like probability distributions feel concrete and memorable.

Studying cognitive science at Rice required Adam to run experiments, interpret data sets, and draw conclusions from statistical tests — so he teaches statistics as a practical reasoning tool, not just a math course. Whether it's regression analysis, p-values, or probability distributions, he connects each topic to real research questions that make the material intuitive.