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Avis et commentaires pour d'étudiants pour Tests d'hypothèses dans la santé publique par Université Johns-Hopkins

499 évaluations
120 avis

À propos du cours

Biostatistics is an essential skill for every public health researcher because it provides a set of precise methods for extracting meaningful conclusions from data. In this second course of the Biostatistics in Public Health Specialization, you'll learn to evaluate sample variability and apply statistical hypothesis testing methods. Along the way, you'll perform calculations and interpret real-world data from the published scientific literature. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values....

Meilleurs avis

21 mai 2020

You have to use outside sources and practice questions to really understand the material. This course makes you think and demands that you know the information. It was a great class. Thank you.

18 juil. 2020

excellant descriptions, good examples and challenging practice sessions. Better if some more were added about ANOVA also. If it is considered as advanced , then it is ok. Good experience

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1 - 25 sur 121 Avis pour Tests d'hypothèses dans la santé publique

par Laura M A

19 nov. 2019

It would be useful to have replies from the professor to the questions in the forum, also more feedback from the quizzes in the course.

par Ji H N

16 juin 2019

To be honest, I think some of the questions as I would find out later in this specialisation could have and should have been worded better. The grammatical or typos make it quite difficult to read the questions. Also I note with some concern that even though we are paying (yes, I agree it is a nominal sum for such a course), we are not getting the feedback and answers to our questions ALTHOUGH the course is still running and not archived. This seems to be a breach of what I signed up for. I do not expect my questions to be answered if it were a free course but I do expect some replies if we are paying for it.

Overall, it is a good course as one would expect from Johns Hopkins but these ?minor errors in grammar/questioning are not what we should expect from a top notch uni. Hope this can be improved.

par Daniel F

13 avr. 2020

Great teacher. Learned the basics of calculating standard errors, confidence intervals, and p values for binary data, continuous data and time to event data.

I would equate this course to an intro level college biostats class. Slightly more about theory than about the calculating formulas (which is good because we use computers these days.

Only a limited amount of R, which I also appreciate.

par Hari K

30 oct. 2019

This is an exceptional course which is very useful for people interested to start their careers in Data Science. It clears most of the confusion and lays the foundation to grow in the industry of Data Science. I have recommended this to many till now

par Ingrid S H C

30 oct. 2019

Although it is a basic theme, the course helped me a lot. I tooke more time than estimated, but i´m happy fot that. There were many details explained by the teacher to whom i gave their importance. Thanks!, Really thanks for this course!

par Bhalchandra V

31 mars 2020

Very well-organized course. Easy to understand. I also enjoyed solving Formative and Summative Quizzes and enjoyed answering to Project Questions.

par Daniel Y T Y

26 mai 2019

Very easy to follow, at just the right level for a non-statistician who would still like to apply this in their professional / research life

par Denise P F

17 févr. 2019

I do recommend this course. Our Dear Professor John McGready has a clear, very objective and highly pedagogic approach to a subject of great relevance for scientific training. Congratulations teacher and thank you - very very very - much for offering us this great opportunity for professional growth!

par Scott F

22 mai 2020

You have to use outside sources and practice questions to really understand the material. This course makes you think and demands that you know the information. It was a great class. Thank you.

par Dr K K

19 juil. 2020

excellant descriptions, good examples and challenging practice sessions. Better if some more were added about ANOVA also. If it is considered as advanced , then it is ok. Good experience

par Anyanwu R

27 mai 2019

I really enjoyed the simplicity of the presentations. I feel I still need to review the materials to ensure it sinks. All in all, This is one of the 3 online courses I have taken.

par Sanchita F

2 oct. 2019

Apart from not receveigin replies to questions on the forum, the course is good and helps explain a lot about how to formulate a hypothesis test.

par Cecil C L

3 mai 2021

For people who want to mix their paradigm with intuition and analysis, through measurement tools and their use, it is the most appropriate.

Without a doubt, with the guidance of Instructor Dr. McGready.

Perhaps it is good to say that just as coins have two sides, so does the course. The latter, in terms of communication with the peer forums and with the instructor, with whom in my case I had no opportunity to contact, without being able to resolve doubts, especially in terms of expression and concepts that are difficult to understand, apply, such as state conclusions (for a non-native of English), not the required calculations. I wish there were more practice of the latter ...

Thankful to Dr. John McGready, Johns Hopkins University and Coursera.

par David M M

6 févr. 2021

Excellent course. Well written. Examples were very helpful to learn. I did like the one slide where John summarized the hypothesis tests to be used for comparisons of samples based on the type. I wish this would have been typed out. While I understood it, John's printing using the electronic pencil is sometimes hard to read. I did make notes in the feature. This is picky but with such an excellent course, it is probably the only constructive feedback that I could give.

I do believe courses like this one are important and fundamental learning experiences for every physician in training or even those of us who have not had the time for formal statistical training.

par Zita Z

27 mai 2020

These statistical courses are so good! I did this as part of the Biostatistical Specialization in Public Health, and it's great help! The videos are very logical and easy to follow, finally I feel that I've gained useful skills. I always used to think that statistics is hard, and I don't have the capacity to learn it, but now I feel a lot more confident and happy about it! I actually started to like it, and enjoy learning it, and for me this is truly priceless. So thanks a lot, I will definitely continue studying the other courses of the specialization as well!

par Darwin F

14 janv. 2021

Excellent course, finally I understand the difference between standard deviation and standard error and how to use the latter in hypothesis testing. Dr McGready's explanation are outstanding, clear and concise. May a recommendation would be to include the non-parametric equivalents of t-tests, z-tests, ANOVA and so forth. Thanks to Coursera, JHU and Dr McGready for this enlightening course. Keep moving forward!

par Kadambari R

20 juil. 2020

At the end of this course, you will have learnt the concepts behind vavrious hypotheses tests, confidence intervals, how to and how not to use and interpret them using several real life examples. Dr. Mcgready does an amazing job of explaining them, such that even a beginner will come out the other end with clarity, and without much struggle.

par Sandeep K

10 juil. 2019

One of the best course to study for the aspirants who are pursuing their career in the field of research want to understand the principle of biostatistics to apply in the research.

Honestly speaking I was confused in the 2nd week but as a whole I really enjoyed.

I would like to sincerely thank Dr. John McGready for creating this course.

par Shane Y

13 juin 2020

This class got a little abstract and it is easy to be confused by some of the terminiology and especially the precision in the terms. However, the professor is a great instructor. Took good notes and repeat the lectures until you grasp the concepts being taught. This was a valuable resource.

par Mariya M

26 mai 2020

Thank you for the clear explanations to all the concepts and the real world examples. I'm a fourth year medical student and refreshing my Biostats before I begin residency. Using everything that I'm learning now to evaluate the clinical significance of the COVID research coming out.

par Ha

4 oct. 2019

Very easy to follow, this course help me to understand a lot of thing that I wonder before like why we can estimate CI 95%, what does it mean. Wonderful course. I extremely like the way teacher talking about complex issues in Biostatistics, very concise and easy to understand.

par Mark S

15 avr. 2019

Excellent course. I have a much better grasp of p-values, t-tests, z-tests, chi-square, log rank tests. Having to read medical literature as part of my job, this course has helped immensely in understanding published results. Simply fantastic use of time.

par Egehan S

11 mai 2020

Successfully summarizes theoretical background for comparison tests used in health sciences. Don't let formulas and numbers intimidate you! You need not work on the mathematics. You can easily complete tests and assignments if you grasp the main ideas.

par Aedrian A

11 janv. 2021

This is a comprehensive introduction to conventional hypothesis testing procedures (for beginners or perhaps those who do not have solid theoretical foundations on the topic), and the quiz/project questions are quite challenging yet enjoyable.

par Deleted A

21 août 2020

This is truly an excellent course in all respects. The instructor effectively presented and drove home the foundational, qualitative bases for statistical hypothesis testing so that the truth of the matter comes out of the numbers.