Chevron Left
Back to Statistical Inference

Learner Reviews & Feedback for Statistical Inference by Johns Hopkins University

4,426 ratings

About the Course

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data....

Top reviews


Oct 25, 2018

Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .


Sep 24, 2020

the teachers were awesome in this course. I liked this course a lot.Understood it properly.Thanks to all the beloved teachers and mentors who toiled hard to make these course easy to handle.Gracious!

Filter by:

776 - 800 of 869 Reviews for Statistical Inference

By Shikha B

May 10, 2016

Teaching material is fine. The Professor's explanation is monotonic and he uses textual definitions rather than simple explanations

By Fabiola J C

Jan 10, 2021

Even though Brian presents the theory in a clean mathematical way, it is still difficult to understand and apply to the problems.

By Ivy W

Nov 13, 2023

It's not for beginners. Concepts are not explained in a clear manner that is accessible to beginner learners.

By Emanuel A F d S

Jul 23, 2017

The course lectures are very confusing. I had to read a statistics book to clarify some of the concepts.

By Mario R

Jan 29, 2016

The instructor is not very good when compared to the others that are involved in this specialization.

By ooi s m

Jun 12, 2017

Lots of formula without detail explanation, not recommended for people without statistics knowledge.

By Fabian H

Jun 15, 2016

Concepts are very hard to understand, even with some background knowledge in statistics.

By Chengde W

Jun 24, 2017

He is just reading the slides. I'd prefer to read a book rather than watch the videos.

By Gareth S

Jun 14, 2017

Assumed a level of knowledge of stats already. Found it went too complex too quickly.

By Mike B

Oct 3, 2021

Most of the homework was not covered in the lectures. There are poor examples.

By Haolei F

Apr 15, 2016

Not beginner friendly, might be good as a refresher for grad students

By Daniel R

May 14, 2016

This course was more like a glossary. Not quite good but practical

By Andrea P

Feb 8, 2023

Not enough deep diving in the theory and math behind most topics.

By Colin B

Feb 2, 2019

Teacher is a bit erratic. It makes the course hard to follow.

By Peter H

May 17, 2016

Poor concepts exposition with a bad teaching method.

By Tony C

Mar 3, 2018

This class is not very well explained.

By Hariharan D

Aug 12, 2017

Pedagogy needs to be improved.

By Zaid M M

Dec 15, 2018

Could be better ...

By Anamaria A

Feb 28, 2017

Too much, too soon.

By Nicolas C

Aug 17, 2017

For beginners.

By Scott W

Feb 24, 2016

Homework, lectures, and the quiz are completely out of sync. Bayes rule is introduced and appears in the homework but no where else. Things appear on the quiz that aren't in the home work or lecture. This was put together from scraps of another lecture, but in an incoherent fashion. When Caffo tells the viewer that they'll need to use other resources, he wasn't kidding. I dropped this the first time when I kept introducing things that completely had not been introduced, took another stats class, then came back and aced it. I don't mind accelerated learning or using other resources, but there's guide for which concepts are needed and where coverage for them can be found. This leaves little recourse but to know stats already, or go learn it before taking this course. Otherwise you don't know enough to even go find the pieces you need. Incidentally, the dude who does the lectures for Khan Academy does a fantastic job and the lectures are a joy to watch, though some people might prefer something that moves less slowly and carefully and perhaps they would prefer something that glosses over the fundamental concepts more. If that's the case, I can't say enough good things about Biostatistical Analysis by Zar but thoroughly, logically categorizing statistical methods with short, clear examples, references to the original research, and building up one concept after another in logical order. The chapters are short, but the first 16 or so should give you a good enough foundation to deal with about any intro stats class. As it is, Caffo's presentation needs some serious testing and remodeling, but there's no indication that it'll match what Khan Academy did regardless of how much work goes in. At best, it's a bitter pill you can swallow if you already know the concepts.


Nov 11, 2017

The instructor is horrible. He does not understand what it takes to explain mathematical ideas clearly. I do not even understand what kind of audience the instructor is trying to target. For the most part, formulas are not derived but just thrown at you. So, watching this course is definitely going to be a waste of time for someone who (like me) want to understand all mathematical details behind the statistical concepts. At the same time, he is explaining thing using very formal language (probably borrowed from some bad math textbook), so do not expect that you will be able to learn things at least at the conceptual level. have a solid background in statistics, so all the ideas covered in this class are familiar to me. Fortunately, I did not have to learn them from Brian's class.

By Boban D

Apr 3, 2018

I thave a M.Sc in Economics and after not using Statistics for a while I took the course to refresh my knowledge. My conclusion is that this module is a waste of time! The teaching skills of the Tutor are not very good (to say it mildly). All the needed materials are there (in theory), but when it comes to statistics, one cannot emphasize enough how important it is to give illustrative examples and plots. This was not done here, either at all, or very badly. When lecturing Statistic, what I want to see is someone drawing a lot of Graphs and explaingn how and why curves shift and how that changes the numbers and tests. This is how intuition is build for what is going on. Otherwise it only becomes dry Stat...

By Denis G

Feb 27, 2016

This course, which is part of Data Science Specialization Course, which is a BEGINNER specialization, doesn't explain as it should to BEGINNERS. They try to explain, complex topics in 3 minutes ... If I didn't need the certificate, I would definitely not waste my time on this course. Youtube videos from khan academy or Brandon Foltz (Statistics 101) are much more valuable, you really get the topic and they are free. The professors didn't want to spend time preparing good material, from my point of view, the preparation is very poor.

The course is more oriented to teach you to be a "data monkey". You know the code you need to write, but you don't get what are you doing ... Where do these formulas come from?

By Philip K

Jan 27, 2016

Very disappointed with how the transition from the old Coursera platform to the new platform has been handled: lots of instances of the "see lecture X" in the quizzes where the reference is now just wrong because the lectures got renumbered, an almost complete lack of community TA/mentors, and no explanations from anyone as to how the new platform works.

Perhaps the worst of all has been the almost complete lack of acknowledgement of any problems from the folks at JHU. This feels like it's just been dumped on the students without any real testing or any appropriate resources to sort out any problems.