Award-Winning Statistics Tutors
serving Portland, OR

Danny is pursuing his MS in Statistics at Portland State, which means he's immersed daily in hypothesis testing, regression modeling, and probability theory at an advanced level. That depth lets him explain introductory concepts — p-values, confidence intervals, distributions — with the kind of clarity that only comes from genuinely understanding what's underneath the formulas. He's been teaching statistics since 2013 and calls it his favorite subject because of how directly it connects to real-world decision-making.

I primarily tutor a lot of math classes. Anything from elementary school through undergrad. High school Geometry? Yep! Real Analysis? Absolutely! Python? Not quite math, you tricky person, but sign right up. I tutor a bit of physics and computer programming as well. Test prep, too. But who am I? I am a math music philosophy nerd. I love Gauss almost as much as I love Bach and I love Bach almost as much as I like Hegel. So chat to me about that stuff if you are so inclined. But you want to just chill and have homework help? That's quite all right, too!

Probability distributions, hypothesis testing, and regression analysis each require a different kind of thinking — and statistics courses often blur the boundaries between them. Channing teaches each concept by anchoring it to a concrete question: what are we estimating, what assumptions are we making, and why does this test answer that question? His range across biostatistics and graduate-level work means he can adjust depth to match any intro or intermediate course.

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.

I am applying to medical schools to attend Fall 2016 and I like to play basketball, go backpacking and volunteer with youth in my free time.

Probability distributions, hypothesis testing, and regression analysis all clicked differently for Malaika after years as a working actuary, where statistical reasoning was the entire job. She brings that applied lens to statistics tutoring, connecting abstract formulas to the kind of data questions students will actually encounter. Rated 5.0 by students.

As a statistics major at Oregon State, Connor doesn't just know the formulas for hypothesis testing and regression — he understands the reasoning behind choosing a t-test over a z-test, or why a particular sampling method introduces bias. He unpacks the logic of statistical inference so that interpreting p-values and constructing confidence intervals becomes intuitive rather than mechanical.

Studying Human Biology at the University of Oregon meant Ben couldn't avoid statistics — from interpreting regression output to designing controlled experiments, statistical reasoning was woven into his coursework. He teaches concepts like standard deviation, probability distributions, and significance testing by connecting them to tangible data sets, making sure students can explain *why* a method works, not just execute it.

I am passionate about helping students when they are feeling stuck or frustrated, especially in math classes. For 2 years, I have tutored Algebra, Geometry, and Pre-Calculus. I have found it is most helpful to reteach concepts in a setting where students can ask questions along the way. When students feel comfortable asking for help and can work through adversity, they are better able to succeed in the classroom and learn valuable problem-solving skills they can use in the future.

I'm currently majoring in bioengineering/pre-med at Rice University. I graduated as the valedictorian of my high school class. My interests include biology, math, and violin performance. I've done neuroscience research at the Jungers Center for Neuroscience Reseach and the Oregon National Primate Research Center. I've also done molecular biology research at the Oregon Stem Cell Center.

I'm a fourth year medical student who will graduate from Georgetown University School of Medicine in May 2025. I have been tutoring since high school in a wide variety of subjects at all levels of education from elementary school to the graduate level. My tutoring philosophy is to identify where and how students are struggling and to use each student's learning style to break down concepts and learn in smaller steps. While I have a wide breadth of subjects in I am comfortable tutoring, I am most experience in the maths and sciences

What separates a strong statistics student from a struggling one usually isn't computation — it's grasping why a particular test or measure fits a particular question. Tessa's mathematics coursework at Yale, paired with her history training where she regularly evaluates quantitative evidence in primary sources, gives her a sharp eye for the reasoning behind tools like confidence intervals and hypothesis tests. Rated 4.9 by students.

Probability distributions, hypothesis testing, and regression analysis all clicked for Crony during his neuroscience research at Brown, where he used statistics daily to interpret experimental data. He brings that applied perspective to tutoring sessions, showing students how each concept works in practice — not just on a problem set.

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.

Interpreting p-values, choosing the right hypothesis test, and knowing when a confidence interval actually tells you something useful — these are the concepts that separate students who understand statistics from those just plugging into calculators. Zachary brings a researcher's perspective from his biochemistry and biophysics training, where statistical analysis was built into every experiment. Rated 5.0 by students.

Probability distributions and hypothesis testing trip students up when the notation obscures what's actually being asked. Alyssa breaks each problem into a concrete question first — what are we measuring, what's the claim, what does the data say — then maps it onto the correct formula. Her math background at Vanderbilt gives her the fluency to make statistical reasoning feel less like guesswork.

Probability distributions, hypothesis testing, and regression analysis all click faster when you've actually used them to make decisions. Hari's finance background means he's applied statistical methods to real datasets — forecasting, risk analysis, variance modeling — and he teaches the logic behind each test so students can choose the right approach on their own.

Probability distributions and hypothesis testing trip students up when they try to memorize formulas without understanding what a p-value actually represents or why a sample size matters. Abismael connects statistical reasoning back to real engineering applications — quality control, experimental design, process variation — which makes abstract concepts like confidence intervals tangible. He's the kind of tutor who will quiz you with problems you haven't seen before, because that's what exams do.

Engineering at Dartmouth meant Rachel lived in data — running experiments, interpreting distributions, and making decisions based on probability and hypothesis testing. She brings that practical fluency to statistics tutoring, connecting concepts like standard deviation and confidence intervals to real scenarios instead of leaving them as abstract formulas.

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.

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.

Understanding probability distributions or interpreting a confidence interval requires a different kind of thinking than most math classes demand. Dillon spent years applying statistics in engineering contexts — quality control, data analysis, experimental design — and he brings that applied lens to topics like standard deviation, z-scores, and regression so students see what the numbers actually tell them.

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.