Null Hypothesis, p-Value, Statistical Significance, Type 1 Error and Type 2 Error

SKIP AHEAD:
0:39 – Null Hypothesis Definition
1:42 – Alternative Hypothesis Definition
3:12 – Type 1 Error (Type I Error)
4:16 – Type 2 Error (Type II Error)
4:43 – Power and beta
6:33 – p-Value
8:39 – Alpha and statistical significance
14:15 – Statistical hypothesis testing (t-test, ANOVA & Chi Squared)

[Goldpoint]

Most explanations of P-Value on YouTube are not clear at all. Some do not include the alpha threshold for the interpretation of the P-value. They also explain that the 5% shows that there is only a 5% chance of rejecting the null hypothesis. This is the best and clearer explanation out there❤

[leahjaelgreen]

Cannot thank you enough I have been struggling with this for two days. Taking online statistics is a bad idea, but you made it very logical and now I understand. Thank you thank you thank you! ❤️

[cait]

Realized I failed my stats midterm because I didn’t properly understand these key concepts. This video clarified so much! Hopefully the next midterm goes better. Thank you for your clear, concise explanations

[hitsuccess]

this is the video tutorial where my concept has been cleared on P-value, TYPE I & II error, Level of significance, and 3 Test. Thank for sharing the video.

[InaKatsCanne]

can't thank you enough! one of the only videos out there that puts it in a health services research/biostats context, appreciate it!

[jluqas]

I can’t thank you enough for this exceptional video. All the doubts in my mind about p-value, statistical significance and the Ho cleared. You are god sent!

[bikegirl]

With much gratitude, I thank you for succinctly explaining p-values, stat significance. I've been struggling for days and am thankful this video popped up in my feed.

[pumpkinpie]

Hi there, I'm completing a research class in my NP curriculum and you've pretty much summed up 3 chapters in a nicely explained 16 min. video. Honestly, statistics is such an abstract topic, but you've managed to clearly explain and correlate concepts in a very logical and understandable way. I will be referencing your video in some of my discussion boards. Thanks for taking the time to put this together. Great job!

[kendallshaw]

You just explained in 15 minutes what my professor couldn't get across in 6 hours of lecturing. Posting this link to my class discussion forum!

[nathaniel]

Thank you ...you saved me almost a week to exams and I am now getting the concepts

[andotech]

This is crystal-clear. Thank you! A lot of us are using this for machine learning to hasten the Technological Singularity. Make sure you're all paying attention and keeping your politicians in check! This can turn out to be a utopia or a nightmare for humanity. Good luck, everyone, and I'll see you on the other side!

[CesarFelipeLopez]

Came here after jumping around some videos looking to clarify what the P-value was. Thanks for the easy explanations.

[chenburnett]

Jesus！So far, the most explicit but in a simplest way to explain these difficult stuff which enables me finally start to understand more about all these tough concepts！Thanks!

[BadlndsBob]

Very informative video. I had this in grad school a LONG time ago and this was a good refresher. It helps me to be certain of how poorly informed (ignorant) some folks I know are.

[shayekhabedin]

the moment i started watching the video - you made me feel so good by calling me a distinguished physician, tysm

[fundeadwarrior]

Explained in a clear, simple and sequential manner. This man is an artist!

[coventrykids]

Literally the best video-tutorial on methodology that I have come across so far! Thank You!

[rebeccatheurich]

thanks, u are far far better than my lecturer in school. i truly am grateful

[taurahdizadare]

Thank you so much for breaking biostats down in a much simpler way for me to understand. I have my exam in 3 weeks and was freaking out terribly behind these concepts. You're a lifesaver!

[oscarwilde]

One thing that I would point out is that a p > 0.05 DOES NOT mean that there is "no statistically significant difference" (see 10:09). It only means that we cannot tell if there is a statistically significant difference.