Statistics and data literacy for non-statisticians

Learn the key terms and analysis methods in statistics
Free tutorial
Rating: 4.7 out of 5 (1,887 ratings)
30,034 students
1hr 36min of on-demand video
English [Auto]

Important terms in statistics
Overviews of the most important statistical methods


  • None!


Welcome to this short course on statistics literacy! The purpose of this course is to teach you about basic statistics terminology and foundational concepts. You will learn the meaning of key terms in statistics, such as p-value, ANOVA, variance, t-test, etc.

If you are brand-new to statistics, then this is the right course for you. It’s a beginner-level course, so if you’ve already taken a statistics course, or read a statistics book, then you might find all of the material covered in this course to be familiar. In that case, you can browse through the course and see if there are some concepts you need to brush up on. You don’t necessarily need to go through the entire course in order — you can skip around to the videos you are most interested in learning from.

By the end of this course, you will feel more comfortable talking about and reading about commonly used statistical analysis methods. You’ll also be able to engage in conversations with people who focus on technical or statistical issues, business analytics, and so on.

Please note that this course does not cover the math of the analyses, nor software to perform statistical analyses. I show a few basic formulas, but the focus is much more on conceptual understanding than mathematical detail.

Who this course is for:

  • People unfamiliar with statistics but who want to learn the basics!


Educator and writer
Mike X Cohen
  • 4.6 Instructor Rating
  • 49,567 Reviews
  • 267,521 Students
  • 26 Courses

I am a full-time educator and writer, and former professor of neuroscience. I "retired" from that position so I could focus my time and energy creating high-quality educational material just for you.

I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I have taught in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >100 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.

I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.

Over 200,000 students have watched over 15,000,000 minutes of my courses. Come find out why!

I have several free courses that you can enroll in. Try them out! You got nothing to lose ;)


By popular request, here are suggested course progressions for various educational goals:

MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing

Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python

Applied linear algebra: Complete Linear Algebra; Dimension Reduction

Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing

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