Online Statistics & Probability Tutor

Who This Page Is For

AP Stats students

You need to learn the AP curriculum cold, including the inference flow chart that decides which test to use. We drill it.

College stats students

Intro stats for any major — psych, bio, business, engineering. We work in the framework your class uses.

Grad students & researchers

You're doing a thesis, analysis, or capstone and need help choosing the right test, running it, and interpreting the result.

What We Cover

Descriptive Stats

Mean, median, mode, range
Variance, standard deviation
Quartiles, IQR, boxplots
z-scores and percentiles
Histograms, stem-and-leaf

Probability

Sample spaces, events
Conditional probability
Independence
Bayes' theorem
Counting, permutations, combinations

Distributions

Binomial, geometric, Poisson
Normal distribution
t, chi-square, F distributions
Sampling distributions, CLT

Inference

Confidence intervals (mean, proportion)
Hypothesis tests (one and two sample)
Type I / Type II errors, power
Chi-square tests
ANOVA

Regression & Correlation

Linear regression and least squares
Correlation coefficient
Residual analysis
Multiple regression
Logistic regression intro

Software

R (base + tidyverse)
Python (pandas, scipy, statsmodels)
SPSS basics
Excel and Google Sheets
TI-84 / TI-Nspire

A 60-Second Sample

How I'd Walk You Through Picking a Hypothesis Test

The hardest part of stats isn't running the test — it's knowing which one to run.

Scenario: A company wants to know if a new training program improves employee productivity. They measure productivity before and after training for 30 employees. Which test?

Identify the data. You have two measurements per person (before and after). That's paired data — each employee is their own control.
Identify the question. "Did productivity improve?" → mean of after − before > 0. That's a one-sample mean test on the differences.
Choose the test. Paired-samples t-test, one-tailed (we hypothesize improvement, not just change).
Common mistake to avoid. Students often run a two-sample t-test (treating before and after as independent groups). That throws away the pairing — and dramatically loses statistical power.
Set up and run. H₀: μ_d = 0. H_A: μ_d > 0. Compute differences, get sample mean and SD of differences, compute t, look up p-value with n−1 = 29 degrees of freedom.

The real lesson: The test isn't the hard part. Identifying the structure of your data (one-sample? paired? independent groups? categorical?) is what determines the test. We build a flowchart you'll use forever.

Common Sticking Points

"I don't know which test to use"

This is everyone's first wall. We learn a 5-question flowchart that handles 95% of stats class problems.

"p-values don't make sense"

You're not alone — even practicing scientists misinterpret them. We use concrete examples to anchor the right interpretation.

"R / SPSS scares me"

We learn the 6 commands that solve 80% of intro problems, then expand as needed.

"My professor uses different notation than my textbook"

Annoying, but solvable. We map both notations onto the same picture.

FAQ

Do you tutor AP Statistics?

Yes — AP Stats is one of the most-tutored subjects. We cover all units and prep with real FRQ-style practice.

Can you help with college statistics for non-math majors?

Yes. Whether it's a business stats, psych stats, or biostat class, we focus on understanding the test or method you need, not abstract theory.

Do you help with R, Python, SPSS, or Excel?

Yes. We can work in R, Python (scipy/statsmodels), SPSS, Excel, or your textbook's calculator — whichever your course uses.

I'm a grad student doing data analysis. Can you help?

Yes. We can work through study design, test selection, model interpretation, and writing up results.