Top 5 Statistics Concepts in Data Science Interviews: P-value, Confidence Interval, Power, Errors

This is really basic... how do jobs require multiple years of experience when these interview questions are just basic thing you learn in an intro stats class... ???

Super useful. One of the best DS videos I have ever seen !

Wooo, smart and elegant lady! Thanks for your video, helped me a lot!

Love the video! Thank you so much for the tips!

Very informative and helpful ❤

Significance (p-value <5%) is the probability that we reject the null hypothesis while it is true.
(probability of testing negative pregnancy for actually pregnant woman)
Power (>80%) is the probability of correctly [rejecting the null hypothesis while it is false.].
(probability of not testing positive pregnancy for male)
for 3 or more outcome, [testing negative] >< [not testing positive].
Significance is thus the probability of Type I error, whereas 1−power is the probability of Type II error.

95% confidence interval shows 95% from the center of a normal distribution population is represented.
ie: 5% outliers are not represented by the equation

I am very grateful for your useful videos! Great content! You are so smart and beautiful! 😇 Also preparing for DS interview, these videos help a lot!!!

Great content!

Very clear explanation, thanks

Great video, helps a lot

13 mins saves me at least 3 hours

Thank you for the video, can you please share another example for p-value in the layman's term?

What if N increase, does it affect P-value?

Hi Emma, thanks for the great explanation, one question though -- how is power used to determine the sample size? I thought the sample size determined the power, i.e. the larger the sample size the higher the statistical power.

Like it!!!!!

Amazing videos Emma ! I am preparing for data science interviews and feel so lucky and grateful that I found your channel ! I am making it a point to follow your advice to the words ! Thank you so much for what you are sharing with us!

You think your thumbnails are so cute!!! Well they are

the higher CL -> wider c.I? Is that a typo? I thought the opposite

给你一个大大的赞！

Extremely helpful. Thank you.

Landed here preparing for my upcoming interview and this is very useful as a revision material as well.

Hi Emma, I have watched a lot of videos you made and they are super clear and helpful for preparing my DS interviews. Thank you so much!

So glad I came across this goldmine of a channel, honestly such great relevant topics with the most useful explanations - I trust you 100% to help with my interviews haha

could you explain the "AT LEAST as extreme as the data is actually observed" in the definition of the p value?

NO one word of bullshit. Appreciate it, Emma.

This is amazing Emma! Thank you so much for such great content. I'm prepping for DS intern interview and your videos literally save me

thank you. it's really helpful!

This is super clear, and now I have a good sense or expectation from the interviewer! Thanks Emma!

So well explained! Thank you Emma! <3

Hi Emma, Can you please share a link to the slides.

作为一个在面试的人，来回来去看了好多次emma的视频了，常看常新。谢谢Emma

Thank you so much!

This is really helpful. Now I know where my mistakes were!

Emma, 可以不可以出一个视频总结一下常用的distribution，有的时候面试的时候被问到sales data是什么样的distribution，我每次都答normal。。。

Non-technical audience!

A comment on the confidence interval, I think your interpretation (and a lot of data analyst) is from Frequentist's point of views. For Bayesian, there is no fixed true value.

What a beautiful lady with high-quality content!

This is really great. I've been thinking about how to explain p value to non-technical person and find a great example for a while. This is definitely very clear! Hope you can continue to make some videos for stats concept like Simpson Paradox etc

I came across your video and it turns out to be super helpful! Thank you! subscribed.

自己复习才发现，Emma真是将这些内容完全吃透，整理成自己的体系。不管是product sense还是stat，全部是干货并且非常organized。多余的废话一句没有（对比我自己的录音回答发现了一堆废话hhh）。非常感谢行业内有这样的领路人。继续期待product sense实例分析/stat & probablity 考点/take home & presentation思路总结和其他DS相关内容！Emma 新年快乐！新的一年身体健康，工作顺利，万事如意！

Great Vid! Follow up question: how do you get a feel for how technical your audience actually is?

Very intuitive video. Please also consider making a video explaining the metrics for regression, classification and clustering machine learning models from both technical and business perspective.

good explanation! better to put non-technical part first

Why did you delete most of the previous movies?

really helpful! Thank you very much for do this! Emma, can you introduce * how to do a project* for the people who want to transfer to data science from other unrelated fields? Appreciate ahead of time!

Great video Emma !! Technical vs non technical explanations were very impressive !!