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.

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.

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.

A PhD in economics at Yale means Anthony doesn't just teach statistics — he relies on it daily, from econometric modeling to designing empirical studies that require careful handling of inference, sampling, and regression. His dual undergraduate background in physics and math gives him an unusual ability to trace statistical methods back to their mathematical roots, making concepts like maximum likelihood estimation or the central limit theorem genuinely intuitive. Rated 5.0 by students.

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.

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.

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.

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.

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.

As experienced and passionated educator with a Bachelor's degree in Computer Science from Rice University, I am passionate about empowering students to achieve their academic goals. With over 3 years of tutoring experience in subjects such as AP Computer Science A, Machine Learning, and SAT Math, I adapt to different learning styles and create a supportive learning environment. My teaching philosophy centers on personalized instruction, where I connect with each student to understand their unique learning styles and challenges. I take pride in guiding students through the complexities of computer science and college application essays, equipping them with the skills they need for future success. Outside of tutoring, I enjoy exploring new technologies and engaging in coding projects to improve my teaching approach.

Interpreting a p-value or choosing between a z-test and a t-test trips up even strong math students because statistics demands a different kind of reasoning than algebra or calculus. Natasha's biostatistics background and MIT engineering training mean she can explain hypothesis testing, confidence intervals, and regression analysis through real experimental contexts. She's rated 4.9 by students.

Probability distributions, hypothesis testing, and regression analysis each require a different kind of thinking — and Rahi distinguishes clearly between the conceptual reasoning and the mechanical calculation so students know which skill a problem is actually testing. His applied mathematics background means he can explain the logic behind formulas like the Central Limit Theorem instead of just handing students a recipe to follow.

A year as a course assistant in Harvard's math department gave Richard a front-row seat to where students get tripped up — and in statistics, it's almost always the jump from computing a value to interpreting what it means. He teaches concepts like variability, correlation, and probability by connecting the math to the kind of data-driven arguments he encounters in his government coursework, where a misread confidence interval can derail an entire policy claim.