Statistics literacy for non-statisticians

Learn the key terms and analysis methods in statistics
Rating: 4.7 out of 5 (204 ratings)
3,173 students
Statistics literacy for non-statisticians
Rating: 4.7 out of 5 (204 ratings)
3,173 students
Important terms in statistics
Overviews of the most important statistical methods


  • None!


In this short course, you will learn the meaning of key terms in statistics, such as p-value, ANOVA, variance, etc.

By the end of this course, you will feel more comfortable talking about and reading about commonly used statistical analysis methods.

Note that this course does not cover the math of the analyses, nor software to perform statistical analyses.

Who this course is for:

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

Course content

5 sections • 18 lectures • 1h 36m total length
  • Types of data: categorical, numeric, etc
  • Populations, samples, case reports, and anecdotes
  • Visualizing data
  • Measures of central tendency (mean, median, mode)
  • Measures of dispersion (variance, standard deviation)
  • Data normalizations


Neuroscientist, writer, professor
Mike X Cohen
  • 4.5 Instructor Rating
  • 17,222 Reviews
  • 87,546 Students
  • 19 Courses

I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.

But you're here because of my teaching, so let me tell you about that: 

I have almost 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 teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >50 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 (look them up on amazon!) 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 nearly two decades refining and perfecting his teaching style.

Over 82,000 students have watched over 6,000,000 minutes of my courses (that's over 10 years of continuous learning). Come find out why!

I look forward to seeing you soon in one (or more) of my courses.


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