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Johns Hopkins University

Statistical Inference

Johns Hopkins University via Coursera


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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.


  • Week 1: Probability & Expected Values
    • This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
  • Week 2: Variability, Distribution, & Asymptotics
    • We're going to tackle variability, distributions, limits, and confidence intervals.
  • Week: Intervals, Testing, & Pvalues
    • We will be taking a look at intervals, testing, and pvalues in this lesson.
  • Week 4: Power, Bootstrapping, & Permutation Tests
    • We will begin looking into power, bootstrapping, and permutation tests.

Taught by

Brian Caffo


2.8 rating, based on 34 Class Central reviews

4.2 rating at Coursera based on 4427 ratings

Start your review of Statistical Inference

  • Life Is Study
    This course is basically an introduction to statistics in R. The course covers many different topics in the span of 4 weeks from basic probability and distributions to T tests, p values and statistical power. The lectures take the form of slideshows with a lot of dense mathematical notation, small text and mediocre voiceovers. The course tries to cover too much ground too fast and the material isn't presented in a way that is easy to understand or engaging. I don’t think the lecturer’s face was shown once in the entire course. That’s not to say there isn't good information in the lecture slides, but the presentation and execution are poor.
  • Brandt Pence
    Statistical Inference is the sixth course in the Data Science specialization, and the first course in the analytical portion of the course (followed by Regression Models and Practical Machine Learning. The course covers probability, variance, distri…
  • Profile image for Bill Seliger
    Bill Seliger
    You'll need to complete this course for the JHU Data Science specialization but you will likely struggle if you don't already have a strong background in statistical inference. There are much better courses that cover this topic - Duke as mentioned above is great. Also, the month I took the JHU course there was zero participation from the staff and TAs, in spite of the fact that several of us reached out to Coursera and the Staff help never arrived.
  • Anonymous
    Not recommended - take Duke University's course instead. Confusing lectures and poor development of homework material. Peer grading was poor due to lack of clarity in the grading rubric. Appeared to be no interaction by instructor or feedback (vice Duke course in which the instructor was highly engaged). Don't waste your time or money.
  • Anonymous
    First of all the course is not easy, especially for a person who have little or no experience in statistics and math-like me-. Anyone starting this course should be aware of this. Some concepts really needs extra time to study after lessons, probabl…
  • Barbara Basberg
    This course and professor get a bad rap in my opinion. The topic can be difficult if you don't have any prior experience with statistics. I believe the professor tries very hard to improve the course over time because one of the earlier complaints was that the videos were just slides with a voiceover. That's not true any more.
    Students are given many study aids such as homework, swirl exercises (r language) and examples that, if a student makes sure to go through ALL the course materials and reading, will pretty much give you the answers to the project.

    This was an outstanding course that does squeeze a lot of stats into a short time frame. It's rather hard but so are a lot of worthwhile things, right?
  • Anonymous
    I was enrolled in the data science specialization with John Hopkins University in Coursera, and this was the 6th class in the program, out of 10. This class is the one that made me drop out of the program entirely. I was able to follow easily the Da…
  • Alain
    To the difference of many I found this course very interesting, difficult for sure and true the lecturer could be fast. You need to spend time with the slides, but if you want to grab inference this is the course. Keep in mind it is a bit as when you are at uni, one hour lesson then 4 hours work.
  • Anonymous
    Pointless - don't waste you time. Especially money. These people plainly cannot teach - but they can take your 49 dollars, they are good at that.
  • Anonymous
    Awful course with really poor lectures - they are confusing even if you know some statistics. The course is not engaging at all and after that I decided not to take any verified courses at all.
  • The course covers quite a lot of material, very quickly. Unfortunately, the material, while nominally for beginners, requires a decently strong statistics background. Even with a good foundation of statistics it was difficult to follow when exampl…
  • Too much content for a few weeks, if you don´t have a clue about statistics, It will be hard.

    Also, the professor isn´t bad, the guy really knows a lot, but the teaching method is not awesome as in the other courses. Maybe a little more didactic (Specially because of the math), would be helpful, more examples and more weeks to cover the whole content.
  • Michal
    The course is a part of very good 'data science with R' program (don't know current name cause it changes) available at Coursera.

    The program is quite massive, it contains about 8 courses but is really thorough and well presented. It is designed with even complete beginners in mind, so may start it without any prior knowledge.
  • AjeethaaL
    I will not be continuing with this course. The lecturer just goes on about 'TELLING' the stats theories but no explanations. Almost minimal.

    If anyone wants to understand Stats esp Inference Stats, go to Duke University's Stats with R. The best! Dr. Rundel explains the concepts so clearly with great examples.
  • The material in the class is solid, but is poorly described. These are the foundations of statistical analysis, and unfortunately there's a lot of statistics jargon that students aren't going to be familiar with in here.
  • Rafael Prados
  • Profile image for Jevgeni Martjushev
    Jevgeni Martjushev
  • Anonymous
  • Karri S

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