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Thank u for all da efforts

I cannot read the slides, please fix it!

Promo-SM 😘

This is the best video on p-value ever I watched.

thank you so much! i have to present a study this week that deals with pet-peese and all those other statistical methods, and this helped a lot!

Very helpful, thank you so much! :)

Wow. Calm and cool with precision. Love you sir.

what is difference between the Bayesian MAP estimator and the frequentist maximum likelihood estimator

3:42 That is an interesting mathematical approach.

Loved it! Thank you!

At 5:50 i am reading Fisher's own words, so I doubt these are incorrect. It is unclear from your comment what I would have said that is wrong - I strongly doubt I made a mistake.

Fantastic explanation

Thanks

Thank you Daniel. Your explanation of the “p” values is the best of all the explanations I have seen. I like your method of presentation. I subscribed! I like your examples. You are an fantastic teacher. I am so glad I found your channel.

Hi Daniel, what do you mean ANOVA 2X2X2? If I have pair wise data T1 and T2 for 10 categories with each category having different number of observations, will alpha for two sample test across category inflate?

How can I present sample size on tables indicating percentages thanks for the response

You make very good videos. Thank you.

Thanks for putting this together.

Oooh, math can be explained in Guru style sign me in!!

I want to do meta analysis....for the first time...pls hlp

Sir could you share your email address

Thank you so much. I learned a lot.

Truly Amazing....

The deadface delivery of the pre-cognition and paranormal distribution joke literally had me cackling i love you so much (thank you for giving us so much amazing free knowledge, from your amazing coursera course in general to all these amazing vdieos and even your blog!!! you're amazing and i appreciate you tremendously)

Marvelous. thanks a ton!

I've been through about 20 presentations on P for my nursing students here in Africa, this is by far the best!

thanks

Are there more videos in this format to come?

This comment goes beyond the original scope of the video, but it is related. Using the binomial distribution to calculate the likelihood of observing certain combinations of significant/non-significant results of hypothesis tests assumes that the hypothesis tests (even under the null hypothesis) are independent from each other. This is a useful approximation in practice, but isn't necessarily accurate for all combinations of hypothesis tests. If someone knows some general theorems on this topic, I'd love to hear about them. Otherwise, you can do Monte Carlo methods to check if two tests are independent under the null hypothesis.

Somewhat implicit in how you introduced likelihoods is that the choice of distribution matters, thus the likelihood (and consequently Fischer information) of a parameter is also given the choice of model. I think the binomial distribution is an excellent choice for this video for pedagogical reasons.

AT 8:29 an approach called "sequential analysis" is recommended. There may be ways to evaluate data 'as it comes in', but the recommendation that was given in this video is not appropriate. If you collect data until you have a statistically significant result, you are P-hacking.

Thank you so much for thinking through this and for posting this video!

Very interesting and clear content! By any chance, is there a place where you talk about the underlying assumptions of these bias correction techniques? Or do you have any ressource to advise?

This is an amazing insight. Thanks!

Thank you very much for such wonderful lectures!

This was very informative and helpful. Thanks!!!

Clear, concise, and to the point. This has to be the best explanation of Bayesian thinking.

Well explained. Thanks, Daniel. Cheers from the Technical University of Denmark!

Very informative 💜 🌿 ❤️

Thank you, Zoltan is such an interesting Scientist. Thank you for this interview Daniel, my Sons also enjoy his boxing instruction.

Thank you!

Amazing, thanks!

Fantastic.... Thanks for sharing.

Why it is said that the smaller the p-value the stronger the evidence against null hypothesis? I thought the opposite to be true. Can someone explain this?

Amazing explanation...I must say Stats is taking us close to philosophy

Great interview

Great job! You explained a frustration of mind very very well! I could never find the words, but you obviously have! Little thing to boost your creditability especially if you are criticizing other scientists, the word "data" is plural.

mamzing!! mama miyaaa

Btw, Thomas Bayes was a priest. Good video though!

Who would dislike this?