Sufficient Statistics and the Factorization Theorem

This video teaches you all about sufficient statistics - what they are, why they're important and useful, and how to find them using the factorization theorem, with examples for the Binomial and Poisson distribution.

[@pfile1]

Thanks for the straightforward explanation!! Now I can understand why "sufficient" is sufficient!

[@phillipmunkhuwa5435]

Great explanation

[@aldenc.9461]

Really impressed with your videos, keep on making more!

[@briangreco2718]

Thank you, many more to come!

[@user-ps6vx8xr6l]

Thank you very much!!!
Very clear, usefull and understandable

[@dolynk]

This is a great, intuitive explanation. Thanks!

[@briangreco2718]

Thanks, glad you found it helpful!

[@DonFranciscoUSF]

This is a fantastic explanation, clear, simple, and short :)

[@briangreco2718]

Thank you!

[@awongiwengxanga7196]

Thank you!

[@briangreco2718]

Thanks for watching!

[@yasamanboroon-zn2lu]

It was awesome please continue 🔥

[@maryziperman4410]

thank you soooooo much. this was so helpful for my college final in mathematical statistics at Texas a&m!!!! you are incredibly gifted!

[@briangreco2718]

Thanks, Mary, I'm glad it helped!

[@TaoLeaf]

Keep up the good work!

[@briangreco2718]

Thank you, lots more to come!

[@raltonkistnasamy6599]

thank u so much man u explained it so so well

[@briangreco2718]

Thanks, glad you enjoyed it!

[@ashsingh2175]

great!

[@raltonkistnasamy6599]

thank u thank u thank u

[@matteomorellini5974]

Thanks for the video, I'm not grasping only one concept: why is the summation of X_i sufficient in the binomial case (I assume this means we won't need the number of trials)? Shouldn't we know the number of successes with respect to the total trials? For example of course the summation of X_i = 3 where n=5 and where n=100 should give different probabilities

[@briangreco2718]

Yes, you're totally correct. We do need to know the number of trials, but that's usually known to us already, so in that case the # of successes is equivalent to the proportion of successes because we can just divide by the (already known) number of trials. (If the number of trials were *also* an unknown parameter that we were trying to learn about, then the number of successes alone would not be sufficient for learning about the probability of success). Let me know if that makes sense or if I can try to clarify further.

[@matteomorellini5974]

@@briangreco2718 yep that's more than 🥁🥁🥁sufficient! Thanks again

[@nimeshamohottige9872]

Great work.Thank you