A very useful course to refresh inferential statistics. If you don't have a minimal knowledge or if you don't remember anything, you will need more time to complete it. The book is clear and there are a lot of exercises, but if you read it and you do the exercises you will need much more time. For those doing it for the data science learning path, R is not very well explained, because this is the second course in a specialization of five courses in Statistics with R. The teacher teaches well.

Well structured course to take at your own pace. I did a stats course about 5 years ago and this has been a good refresher - not sure how hard it would be for a total novice - i think it would take more time than suggested. Warning, if like me you have prior experience in R the assignments will take a little more figuring out too. The discussion forums have most the answers and help you need and actually the peer-review is really helpful to 'learn by teaching'.

Some parts are needed more clarification. In other words, as a student of the course you need to go beyond the materials, since the materials are not self-sufficient. Specially about simulation methods. However, this is not the reason that I give the course 4 out of 5. The absence of any help from TAs, based on my experience, is the reason. I expected some official replies to my question while there are only a few question for each week of the course.

Great material although I will like to know more about the practical side of statistical inference. For instance, I have more of less an idea of how to use chi-squared test with categorical variables in a dataset however, for the other statistical inference methods such as p-values and confidence intervals I still don't see where can I use this methods when doing data analysis. Can we use this to detect outliers in our dataset for instance?

Better than the Basic Statistics offered by the University of Amsterdam. That course was too informal, didn't address the techniques and covered too few materials. I love the fact that there are accompanying R labs. However, the course should teach the students the more general R functions (qt,pt,qnorm,etc.) instead of the self-developed "inference" function. In addition, it's a little hasty in week 4. The pace should slow down.

The course is well designed, and the examples given in each lesson are informative and interesting.

For the final project, I wanted to group some categories from one variable together in a new variable, but I did not have the code I needed to do it. It would have been very helpful to have that information in one of the labs prior to doing the final project.

Thorough treatment of the topics with great examples using real data. On the down side, the treatment of the mathematics behind the formulas is a little light. Great use of simulation to support the theory or to use when theoretical assumptions are not met. Strongly recommended!

I loved this course. As with the previous course a lot of things that weren't clear to me before are now. I totally recommend it to anybody new to statistics or anybody who is struggling with statistics (like I have for a very long time).

Very good material which gives practical knowledge supported by interesting examples. The only concern is that it is slightly shallow - lacking some mathematical justification for the given "rules of thumb" and theorems.

Very good introductory course for inferential statistics. It is wise to complete the first course Introduction to Probability and Data of this specialization before enrolling into this one to grasp the concepts.

This course seemed unbalanced compared to some of the other courses and was very work-heavy. I felt it could have, or maybe should have been broken up into two courses, or that other courses should be longer.

The final project does not help, for example someone used discrete data 1,2,3,4,5 .... ,40 to compute a p.value as if it was normal. It is too general and does not fill the purpose of the course.

I love this course. But I have a little bit hard for using the R program. If there is more instruction about using the R program, this course would be a lower hurdle for users likes me.

The course is very well explained I had to refer other materials for ANOVA technique to understand it better hence that part can be either improved OR more reference material be provided

Very useful course about statistics. May need some fundamental understanding of statistics before, but through the clear explanation and examples, I've learnt a lot from this course

This is a wonderfully curated course if u follow the readings and practise suggestions. But the main issue is the R programming. It needs better practise than suggested readings.

Very good course on basic understanding of inferential statistics. Instructor was very clear in delivering the content. The lab work is very helpful in developing R knowledge.

The teaching is good, the course is a little heavy and a lot to take in in the later weeks. But, as a further grounding for statistics and R, I would very much recommend it.

It is a very nice course, I have learned a lot. However, it is convenient to take the previous one of the specialization, as they base some examples or R knowledge on it.

Without a background in confidence intervals and hypothesis testing, I think that it would be very difficoult to understand these concepts in those few videos.

This course gave me the toughest time of my life. I did the course for 3 months, i failed project once but i thank God, i proved toughest by passing with 100%.

The inference function and hypotheses tests are really useful. Permutation tests need more explaining and examples; otherwise they should not be included.

A good course for learning statistical inference, though I found that more than a week per module was required to really absorb the content.

It is a great course, while some underlying logics are not clearly explained. And the quiz has some unexplained context, which is confused.

It was a very interesting and useful course. To improve it a little bit, I would focus more on the use of R to do different analysis.