Interpreting Confidence Intervals EXPLAINED in 3 Minutes with Examples

[@gamingSlasher]

Super good. Finally I got it. It is surprising that statisticians have such a hard time explaining these concepts so "normal" people (engineer) can understand them.

[@RubinhoX360]

Thank you so much! This explanation was immensely helpful

[@mosama22]

Man you are a LEGEND. I really wish you have more videos for statistics. Thank you! 😊😊

[@AceTutors1]

Thank you so much for your support! I'm working on getting some more posted! Just very busy!

[@VenkataSaiMounishEnnamuri]

please do post more of these interpretation videos. they really are helpful !!!!!

[@AceTutors1]

Thank you! We definitely want to put out more soon!

[@a_programmer2754]

Bro, continue please, Your videos are so helpful and clear. THANKS!

[@Vladyslav_Rehan]

Oh, you have such high-quality videos, it's a pity that there are so few subscribers ...

[@AceTutors1]

I appreciate your positive feedback! We hope that through your and our other viewers' support we can grow to help more students!

[@dovidasm]

Thanks for sharing! Although the 2nd interpretation is the right one, I find it really akward saying kind of: "From the sample we calculated the range 7.5-8.5 hours; we are likely to have the true mean in there; I cannot tell you the probability, but ranges obtained this way have 95% chance of having the true mean inside". So, yeah, I totally agree with the 3rd version saying: we are 95% CONFIDENT (warning key word) that the true mean falls between 7.5-8.5 hours. However, I think there's a 4th version which might be useful: "Any mean between 7.5 and 8.5 is "compatible" with the observed average at a confidence level of 95%, i.e., the 95% probability interval for any mean between 7.5 and 8.5 contains the observed average". Hard to put it into words, what I mean: [7.5-8.5] = {μ : | observed_average - μ | ≤ 1.96 σ/√n}. μ is fixed and unknown, it's clear we have no randomness in this set, we cannot therefore calculate a probability, but there's still this 95% probability issue in its construction. So, it might make sense to refer to some kind of pseudoprobability for this set, let's call it ""CONFIDENCE"" 😛

[@chrmilling]

Thank you so much!

[@anthonycarbonaro7890]

Your presentations are excellent. Thank you 🙏. Looking forward to seeing more from you.

[@Goldpoint3295]

Thanks for this clear explanation!

[@mainakdey3893]

this is an awesome explanation. thanks sir 👍👍

[@AceTutors1]

You are very welcome! Thanks for watching!

[@murielsamuels4370]

Thank you. I am telling everyone about you. Thank you.

[@AceTutors1]

Thank you so much for you kind words! I really appreciate your support!

[@gooddeedsleadto7499]

Simple explanation. Thankful Could u also solve few problems to see how it is used and also using p instead of mu? Could u explain how to use it to predict election results through confidence intervals?

[@medhatkmeel1712]

Really, I am ok with the interpretation, BUT why in the fig. shown (1:42) there are some confidence intervals of different length? >>> How the same formula (for ex.: x-bar +/- critical val. * SE) would generate intervals varying in width in the presence of only one x-bar (the sample mean as our point estimate) and only one critical value (reliability factor) that determined by the confidence level we choose and lastly one SE the depends upon the std and the sample size?

[@chayaya949]

You're amazing!!!

[@meq8830]

thank you sooooooooo much

[@vipin_rahul]

Thanks.

[@tushardhondge7334]

thanks bro

[@ryannaehr3062]

Nice video!

[@AceTutors1]

Thank you sir!

[@799lipe]

But are they 'approximately the same thing,' or is the problem just choosing the right words? By the way, thanks for the video; it helped a lot!

[@arthurrichard3766]

Please solve this example questions in your videos.

[@nature_through_my_lens]

Can you do a video on Maximum Likelihood Estimation?

[@leomorlacos4457]

Thank you so much! Why no patreon account?

[@AceTutors1]

Hi Leo! Thanks for your feedback! We believe in producing this content so it is free for anyone that needs it, so we don't have a Patreon account.

[@quantstyle6448]

That second interpretation "If we repeatedly found confidence intervals..." is not an interpretation of 7.5 and 8.5 hours. It's a general definition.

[@AceTutors1]

Great point! Yes it is a general interpretation of the confidence interval that is true for all confidence intervals.

[@maxywaxymus]

Then what do statisticians mean by the word 'confident'? What is making them 'confident'?

[@spyhunter0066]

You haven't mentioned about how to find the confidence interval. As far as I am aware, it's the value we choose to decide, right?

[@AceTutors1]

Yea this video was only meant to go over how to interpret confidence intervals once you find them. In future videos we will go over how to find them for different scenarios.

[@reetikagoel5579]

please make other videos like for annova and chi square distribution

[@AceTutors1]

Those are definitely a couple topics that are in the works! Thanks!

[@reetikagoel5579]

video for hypothesis

[@AceTutors1]

Thank you for the feedback! We are going to tackle those topics soon.

[@armandmatossian2081]

Yes! 100% agree with these comments. The semantics of statistics is such bullshit. It makes learning extremely convoluted. This is a great example of poor teaching about semantics. I need to apply statistics in my engineering job and can’t find a video that is flooded with semantics. Just make it simple. It doesn’t need to be like this.

[@seliassiva9573]

This feels more like arguing about semantics more like. Maybe we should ask the question, how wrong is the wrong answer? Does it actually affecting the final result by a significant amount or can it potentially lead to false calculations?

[@Goldpoint3295]

🎉

[@hhykk]

feell like im still dumb.

[@AceTutors1]

No, please don't think that way! Statistics is a very complex and tricky subject. Just keep working at it and I'm sure you'll be able to conquer it!