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About this course
This course covers the statistics that data interviews and data work actually lean on: reading a distribution, quantifying uncertainty, testing a claim, and knowing what a result does and doesn't prove. You'll work through multiple-choice questions on probability rules, conditional probability and Bayes, sampling and the central limit theorem, confidence intervals, hypothesis testing, A/B test design, correlation, and regression basics. It's for aspiring data analysts and scientists, and for anyone facing a stats round in an interview.
Why practice statistics
Statistics is where confident-sounding wrong answers thrive — misreading a p-value, treating correlation as cause, or trusting a result from a sample too small to say anything. Answering questions forces a commitment, and the explanation shows you which intuition led you astray. That's how the reasoning becomes reliable rather than half-remembered.
How this course works
- Short MCQ sessions you can finish in a coffee break
- Every answer comes with an explanation of why it's right and why the alternatives fail
- Scenario questions built on experiments, samples, and business metrics
- Difficulty ramps from definitions to interpretation and study-design calls
What you'll walk away with
A solid feel for how sampling variation behaves, the ability to state plainly what a test result means, and enough repetition that the classic traps — p-hacking, base rates, small samples — stand out immediately.
Data Analyst series
The complete analyst toolkit on Abekus — spreadsheets, SQL, Power BI, and the statistics behind every conclusion. Four courses that follow the shape of the job: pull the data, query it correctly, present it so a decision follows, and reason about what the numbers actually prove. Free MCQ practice with instant explanations, designed as a progression: each course builds on the one before.
