Get started with custom lists to organize and share courses.

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

# Statistical Inference

32 Reviews 735 students interested
Found in
• Provider Coursera
• Cost Free Online Course (Audit)
• Session Upcoming
• Language English
• Certificate Paid Certificate Available
• Effort 7-9 hours a week
• Start Date
• Duration 4 weeks long

Taken this course? Share your experience with other students. Write review

## Overview

#### Class Central Tips

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.

## Syllabus

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.

Brian Caffo

## Reviews for Coursera's Statistical Inference 2.8 Based on 32 reviews

• 5 stars 13%
• 4 stars 22%
• 3 stars 22%
• 2 stars 22%
• 1 stars 22%

Did you take this course? Share your experience with other students.

• 1
Karri S
4.0 4 years ago
completed this course.
1 person found
Life S
2.0 5 years ago
completed this course.
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.
14 people found
Brandt P
3.0 3 years ago
by completed this course, spending 2 hours a week on it and found the course difficulty to be very easy.
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, distributions (normal, binomial, poisson), hypothesis testing and p-values, power, multiple comparisons, and finally resampling. Overall this is a rather poor introduction to statistical methods, and the only really relevant hypothesis test covered is the simple t-test.

This is the first course taught by Brian Caffo, who is more mathematicall…
1 person found
Bill S
2.0 4 years ago
by completed this course.
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.
8 people found
Anonymous
1.0 4 years ago
completed this course.
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.

8 people found
Barbara B
5.0 4 years ago
by completed this course, spending 8 hours a week on it and found the course difficulty to be hard.
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?
2 people found
Anonymous
4.0 2 years ago
completed this course.
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, probably it would be better to start from a really basic book like "statistics for dummies". That was what i did actually. I needed to study for a couple of months to have a good understand of this lesson. But at the end i started to get familiar with statistical language and satisfied with the course. There are many real life examples, swirl classes and books provided. Ofcourse it could have been better with a better and detailed design of content but yet i am satisfied with it, and i can recommend it to anyone who wants to deal with statistics.
Anonymous
1.0 5 months ago
is taking this course right now.
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 Data Scientist's toolbox, R programming, Getting and Cleaning Data, Exploratory Analysis and Reproducible Research, as the classes were laid out in a good manner, and the assignments and projects reflected what was being taught.

Mind you, I have a programming background already but didn't know R prior to this pro…
Alain
4.0 4 years ago
completed this course.
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.
3 people found
Anonymous
1.0 4 years ago
is taking this course right now.
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.
3 people found
Anonymous
1.0 4 years ago
is taking this course right now.
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.
1 person found
Brett W
2.0 2 years ago
by completed this course, spending 8 hours a week on it and found the course difficulty to be hard.
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 examples are presented quickly and referenced back to material covered in prior lectures or even weeks as if they ought to be totally fresh in the student's mind. I found it most frustrating that when I was struggling to grasp a concept, the instructor would say something like, "It is obvious that..." or "everybody knows..."

There is a lot of material, and if you have time to go and learn everything that is covered you should be good with this, but don't expect to learn the subject just by watching the lectures and doing the swirl exercises.
Daniel R
2.0 4 years ago
by completed this course, spending 6 hours a week on it and found the course difficulty to be hard.
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.
Jason C
3.0 4 years ago
by completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
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 P
2.0 4 years ago
completed this course.
2 people found
Jevgeni M
3.0 4 years ago
completed this course.
1 person found
Anonymous
1.0 4 years ago
is taking this course right now.
1 person found
Kuhnrl30 K
3.0 4 years ago
by completed this course.
1 person found
2.0 2 years ago
by completed this course.
Jan T
4.0 4 years ago
by completed this course.