Award-Winning Statistics Tutors
serving St. Louis, MO

Most students memorize the formulas for z-scores or standard deviation without ever seeing where they come from — Kathleen's math degree from Washington University means she can derive them from scratch and explain each piece along the way. She treats every statistics concept as an extension of the algebra and calculus her students already know, which makes new material feel like a logical next step rather than a disconnected set of rules.

Studying economics at TCU meant Mason lived in statistics — regression analysis, probability distributions, and hypothesis testing were core to nearly every upper-level course he took. He also tutored statistics through the university's math department, so he's seen where students typically get stuck on concepts like p-values, sampling variability, and conditional probability. That combination of applied use and teaching experience makes him especially effective at connecting formulas to what they actually measure.

Seven years of experimental psychology research means Anna has designed studies, run ANOVAs, interpreted regression outputs, and wrestled with effect sizes in contexts where getting the statistics wrong means getting the science wrong. She teaches from that practitioner's perspective — walking through concepts like hypothesis testing, p-values, and confidence intervals as tools for answering real research questions, not just textbook exercises. Rated 4.9 by students.

A self-described sports analytics enthusiast who digs into box scores for fun, Jordan approaches statistics with genuine curiosity about what data can reveal. He unpacks concepts like probability distributions, hypothesis testing, and regression analysis by grounding them in real datasets — the kind of context that makes standard deviation feel less like a formula and more like an insight.

A lot of statistics confusion comes from not seeing the logic behind formulas for standard deviation, probability distributions, or hypothesis tests. Hannah's mathematics degree gives her the ability to unpack the reasoning underneath each calculation, connecting abstract notation to concrete examples. She treats statistics as a way of thinking about data, not just a set of procedures to memorize.

I am a High School Science Teacher for the Smithville School District. I have experience teaching AP Chemistry, Honors and General Chemistry, and Honors and General Biology, Forensic Science, Biomedical Science, and Anatomy & Physiology.

I am a recent graduate and a new mom. I have experience in tutoring undergraduate Physics for about 5 years. I am also interested in tutoring Statistics and Mathematics. It is exiting to be a tutor and share my knowledge with others.

I am from Chicago, IL. I am currently a second-year medical student at the University of Missouri-Kansas City. One of my passions includes teaching students. I have 5+ years of tutoring experience. During my tutoring sessions, I utilize critical thinking skills, enthusiasm, and vigor in helping the students complete assignments. Outside of tutoring, I enjoy traveling, cooking, dancing, and playing volleyball. If you need help with any of your classes, please do not hesitate to set up a session with me!

I am a medical student at the University of Missouri-Kansas City. I love to work with students to help them reach their fullest potential.

I'm not taking care of my farm, I enjoy all crochet, embroidery, cross stitch and, quilting. I also enjoy building with Lego. My largest build so far is the Star Wars AT-AT!

Probability distributions, hypothesis testing, confidence intervals — Ján breaks each of these down by connecting the math to real decisions the numbers are supposed to inform. His I-O Psychology master's program was essentially statistics-intensive by design, and he spent his grad years tutoring Business Statistics alongside his own coursework. That dual perspective means he knows both where students get stuck and why the concepts actually matter.

Probability distributions, hypothesis testing, and regression analysis all require a kind of structured thinking that Florence sharpened through her computer science degree at Duke. She teaches statistics by grounding each concept in real data scenarios — building intuition for what a p-value actually means before diving into formulas. Her 5.0 client rating speaks to how well that approach lands.

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.

As a Statistics major at Northwestern, Jake lives in this material daily — regression analysis, probability distributions, confidence intervals, and hypothesis testing are part of his coursework, not just something he once studied for a test. That proximity to the subject means he explains concepts with the kind of fluency that comes from constant use. He holds a 5.0 client rating.

Understanding when to use a t-test versus a z-test, or why a sampling distribution behaves the way it does, requires more than formula sheets — it takes genuine statistical intuition. Brian built that intuition through his economics coursework at Caltech, where statistical analysis was a daily tool, and he walks students through each concept with concrete data examples.

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.

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.

Understanding statistics means learning to think critically about variability, probability, and what data can actually tell you. Tashina applies statistical methods daily in her PhD research in brain sciences — hypothesis testing, confidence intervals, regression — and she unpacks each concept by connecting it to the kind of real analysis questions that make the material stick.

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.

The IB Mathematics program threw Kinjal into statistics early — designing internal assessments that required real data collection, chi-square tests, and interpreting results under strict analytical standards. That experience, combined with her biology training at Texas A&M where statistical analysis underpins every lab report, means she teaches concepts like correlation, sampling, and variability as practical tools rather than isolated formulas. Rated 5.0 by students.

I am currently working in a Bronx Public School as a teaching apprentice in Algebra. I have four years of experience tutoring one on one with students of all ages.

Probability distributions, hypothesis testing, and confidence intervals require a different kind of mathematical thinking than most students are used to. Nicholas pairs his applied mathematics background at Johns Hopkins with real problem-solving instincts, teaching students to interpret what a p-value actually means and when to apply which test. He's especially effective at connecting statistical reasoning to the kind of data analysis students encounter in science and engineering contexts.

Probability distributions, hypothesis testing, and confidence intervals all require a kind of careful reasoning about uncertainty that Allen sharpened through his economics coursework at Yale. He teaches statistics as a way of making arguments with data — interpreting p-values, choosing the right test, and understanding what a result actually means in context. His 5.0 rating speaks to how clearly he communicates these ideas.