FDR, q-values vs p-values: multiple testing simply explained!

Why is multiple testing a big issue in biostatistics? In this video, we will explain why multiple testing is so dangerous when analysing large datasets, and how to correct for it. We will cover some of the most common methods: Bonferroni correction, Benjamini-Hochberg (BH) and q-values.
Don't let the monster of multiple testing eat your data!
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Watched it already? If you liked this video or found it useful, please let me know! Your comments and feedback are very much appreciated😊 If you have questions, don't hesitate to leave me a comment down below, I will answer as soon as I can:) --------------------------------------------------------------------------------------------------------------------
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More multiple testing resources:
Check the difference between different multiple testing corrections in R:
A really cool explanation of the FDR from Statquest!
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Attribution 4.0 International (CC BY 4.0)

[jorgea.servert]

This video is brilliant! You are a natural at explaining statistics. Thank you so much!

[svetlanavasileva]

It’s gorgeous !!! Please do more about biostatistics

[anmolpardeshi]

this is an awesome video! Applaud the simple and fun explanation. just two things: (a) the "coffee" being NOT associated (among the significant outcomes) comes from a prior knowledge. but we might not always have this prior knowledge - then what do we do? (b)its not shown how the adjusted p values were calculated if you can pls make that clarification. otherwise this is a good video! Thanks.

[ZLYang]

At 1:29, if you find a link, why p is still larger than 0.05?

[cowboycatranch]

The P value for the red smarties still says P > 0.05 (1:28), whereas it should be P < 0.05. Same for 2:12.

[anphan]

Shouldn't it be 1/16 at 7:07, since we have 16 objects being marked as significant?

[carlosdomingues]

Thank you for this great concise video, you can tell you put alot of work into it =] ..Any follow-up on red smarties linked to baldness??

[emotaph]

Thank you for this video and the effort that must've gone into this. Everything you explained was very easy to understand.

I had a question:
You spoke about "correlations" in the video but what about relations one way to the other such as regressions where we speak in terms of "dependent" and "independent" variables. In the examples you shared, the genes would be independent variables and we want to see their relation with the "dependent" variable of being a morning person. Now if we were to check if 1 gene in particular (independent variable) affects different things (different dependent variables)- blindness, baldness, wakefulness, color blindness, etc. would the same logic of q values hold?

It would be lovely if you get the time to get back to this. If not-thanks anyway for the great video!

[shiyiyin]

start watching at 7:00 intro is too long

[Nikolaj-qzkw]

The person in red was asking if smarties cause *blindness, not *baldness :)

[sanjaisrao]

Thanks, I finally understood something about p value and FDR

[ankushjamthikar]

This video is very good! You explained it in a nice way. Thank you for the video. Keep posting more videos on biostatistics.

[SmiladaXD]

Thank you so much for this video. Could you please just clarify how you calculated the P-adjusted values/Q-values? I've been looking everywhere for that and would truly appreciate if you can explain that to me.

[逍遥江湖载]

thanks, you make me truly understood q_value

[artarz]

How do you determine the number of false positives? What are the criteria?

[dome]

i did not get if q-value is more stringent than FDR. I had an analysis in which I used FDR for gene expression, but I think the results are too stringent un confront of difference I observed by experiments and to have a good G0 ontology analysis that represents the biological process going on. what to do in this case?