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4.4

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80 avis

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.
We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software.
For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test)....

par ND

•Feb 13, 2018

Incredibly dense (which they warn you about) so the lecutres fly over so much important info it's hard to keep track of even with a strong focus. A very good overview though.

par PA

•Jul 04, 2017

Hi, I enjoyed really well and this very good course on Inferential Statistics. My experience was really good. Thank you for providing the course for free!

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74 avis

par Oluwatobi Makinde

•Apr 14, 2019

Enjoyed it. The tutors are amazing in how they help you understand each topic and the workings around it.

par Syberen van Munster

•Apr 07, 2019

I took this course as a follow up to "basic statistics". The course is dense and fast-paced, so that's something to prepare for. Here are my observations:

The good:

The R labs are a lot better compared to basic statistics, where they were a disaster. You'll put to use the built-in functions in R to calculate your results.

Also the general amount of information is nice, I feel like I learned a lot about inferential statistics.

The bad:

Sometimes the videos are too fast, functions are shown for 2 seconds not allowing time to absorb the material. I often have to go to other sources to clarify what was meant.

Also frustrating is that there's no feedback on the exams, you're left to guess what you did wrong. Multiple times I found out that it was a rounding error, but the amount of digits are not specified in the question, so you have to re-take the exam several times until you find the expected amount of digits.

Finally, some of the required formulas are not included in the "formulas and tables" document. I hope this will be fixed, since this is essential to passing the course successfully.

par Ute Thiermann

•Mar 13, 2019

Week 1 to Week 5 were well explained, however the quality of the videos declined steadily to the end of the module. Many inconsistencies between video - audio - written text.

Most upsetting was quality of Week 6. In comparison to the parametric statistic parts which were super well organised, structured in assumption - hypothesis - test statistic and anything else important, the Week 6 was terribly confusing. It did not contain the most important formulas and explanations (I was researching online on other fora to reply to the quizz). I suggest a full review of non-parametric statistics, as it is an important chapter for many social scientists working with smaller sample research. I really think this has been neglected over the previous parts.

Also in comparison to Module of Basic Statistics, this one has not had a great quizz section. There are no hints of what might have gone wrong when answers are wrong, so the learning effect is 0 if we get answers wrong.

par Saurabh Priyadarshi

•Jan 07, 2019

Really a great course. It covers almost everything that is important in the subject. But maybe it could have been made better by adding the exercises more often than it is now as it covers a lot of syllabus.

par Muhammad Ali Rabbani

•Dec 28, 2018

loved the way, you carried a statistics virgin like through the course

par gerardo reyes guzman

•Dec 04, 2018

This course is very helpful for people intereted in quantitative research

par Arman Badiei Khorsand

•Nov 13, 2018

Completing this course requires perseverence but it is 100% worth it. There's a lot of material covered and the videos simply provide signposts for the topics but one doesn't learn statistics by watching videos. The real learning takes place during quizzes and assignments. The final exam is time consuming and tough and students need to truly master the material to earn a high grade.

par Zainab Hashimi

•Oct 25, 2018

This course was relatively difficult for my slow brain. I learned A LOT. but I still feel the need for doing this course one more time

par Dragos Ailenei

•Sep 14, 2018

The course is too compressed in my opinion but if you make it past the second week, the learning curve is not that steep anymore.

par norbert boruett

•Aug 07, 2018

wonderful

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