COJohns Hopkins University · on Coursera
Statistical Inference
Intermediate 54 hours English Course Certificate Certificate
About this course
Part of the Johns Hopkins Data Science Specialization, this course covers statistical inference from first principles: probability theory, expected values and variance, common distributions, the central limit theorem, confidence intervals, hypothesis tests, and p-values.
All concepts are implemented in R with practical examples, grounding abstract statistical theory in data-driven analysis. The course addresses common misinterpretations of p-values and statistical significance — essential critical thinking for any data professional.
What you'll learn
Calculate probabilities using common statistical distributions
Apply the central limit theorem to make inferences about populations
Construct and interpret confidence intervals correctly
Perform and interpret hypothesis tests including t-tests and chi-square tests
Recognize and avoid common statistical inference mistakes
This course includes
54h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
BC
Brian Caffo / Roger Peng
Coursera instructor
380K+ learners10 courses4.4 instructor rating
Taught by Brian Caffo, Johns Hopkins biostatistics professor and prolific Coursera instructor known for rigorous yet accessible statistical education.
Requirements
- Basic probability and R programming fundamentals
Who this course is for
- Data scientists who want statistical rigor behind their analysis
- Analysts who use p-values without fully understanding what they mean
- Researchers who need to design and analyze statistical studies
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
Frequently asked questions
It's a solid foundation — you'll also want regression (covered in the next Hopkins course) and Bayesian statistics for a complete statistical skill set.
Moderately — calculus-level math is used in places, but the focus is on intuition and correct interpretation rather than derivations.