The tradeoff between sensitivity and specificity

[@RaynorsProphet]

You achieved in 12 minutes what my lecturer couldn't achieve in 3 lectures, thank you :)

[@fatimajamil4374]

Same

[@dhiaawad6710]

same here. i had two lectures over that.

[@rgvn5]

3 lectures LMAO mad

[@frankielouorganicwear3558]

hahah same!!

[@joecaz]

Best explanation ever! I've looked at multiple descriptions over the last 4 to 5 years, and I was always failed to grasp the concepts completely. I can stop looking for answers now. You have clarified these ideas beautifully. Thank you!

[@ec3539]

I knew from first few words spoken in your articulate and careful manner that your explanation would be worth listening, and I listened and benefited. Thank you very much. Great teaching skills.

[@hollaachagurl92]

Its 2018, and this video is still making an impact!! Thank you so much!!!

[@snekjuce]

@@lekhar5164 2025 now.

[@lhsn2881]

Thank you very much, I had taken 3 courses of epidemiology and this the first time I understand very well the tradeoff btw sensitivity and specificity, thank you again. God bless you!

[@nachiketgotphode8800]

You have done a great job. Greatful to find you.

[@andreashimko7464]

Fabulous lecture! Thank you so much! The visuals you learned to explain this did so much more than any 2x2 table I have ever seen.

[@pamg2628]

Very beautifully explained with great visuals! Thanks Dr. Patwari.

[@melvingeorge2008]

excellent video... clear voice..must watch video

[@zahraaammar2708]

this is a truly GREAT and CLEAR explanation !! thank you so much

[@VinceDacer]

Thank you so much for the clear explanation Mr. Rahul!

[@hodigereananda3036]

Rahul , You have made it easy to understand ROC curves. Have followed your videos wheneverI need to understand basics

[@hannahhannahb9650]

excellent presentation. Nice and slow, and very clear

[@nirjhar8353]

Very well done! I sense it clearly now about the importance of the tradeoff! Thanks.

[@yanyenleungl157]

this binary category is actually so easy to understand. Thank you for explaining it in such a way, Rahul!

[@jonahansen]

Best explanation of the medical sensitivity/specificity lingo in terms of the underlying signal detection theory of Green & Swets. I wish they would just use the original labels of hits, misses, correct rejections, and false alarms for the regions, and d' and beta for the test characteristics.

[@ocean694]

Excellent ! Much better than my professor.

[@CG-rr3wi]

Excellent explanation. Thank you so much!

[@iloveno3]

Best explanation. Very intuitive.

[@KaennC]

While almost any online lesson/lecture made by indian people is top notch, it's often buried under thick illegible accent. Your voice is crisp and you've found an efficient way to explain this. Thank you.

[@ethanmaxwell2235]

ironic that you typed illegible while meaning unintelligible in a comment critiquing others' use of the English language

[@majnooonana]

very good explanation .. I'm specialist and I like how you present this!

[@michellerudolph3425]

Perfectly put! Very clear and concise. Thank you!

[@coolbrowser2008]

You explained very neatly. If you have time please put more. Thank you

[@kasonk1692]

GREATE Lecture in just 12 min!!!!

[@stutisinghal1514]

those who teach simply actually know the concept ...u proved u 👏

[@biotubeinfo]

Awsome. I finally understand since you do understand what you are talking about. Thanks a lot

[@gl5183]

What a superb explanation!

[@saidasaid9565]

Thank you for your great explanation

[@ShinAoki1]

Fantastic video. Thank you.

[@newbie8051]

Great choice of examples !!

[@ffidmetham]

amazing explaination that i am looking for

[@dhisney]

BLESS YOU, OH KIND SIR! MAY YOU ALWAYS BE UNDER GOD'S PROTECTION!

[@samisami25]

Thank you Rahul, bliss you May God give you Paradise :-)

Oh... Thank you so much! Why are other people not able to explain it in such a way like you. LOVE YOU ;-)

[@rocketrider1405]

I wish my prof had explained it this way. I really like this.

[@eddiechapman]

Great job! Very helpful and clear.

[@DOC7ORT]

Excellent demonstration

[@studson112]

This was a great explanation - a perfect crash course for review. Thank you!

[@germangarcia9260]

That was a great explanation. Thank you!

[@rehabalsaleh166]

Absolutely amazing! Thanks a lot!

[@NnaemekaNwoke]

Amazing presentation of the content!

[@premminchu]

Thank you so much for this perfectly done video!

[@francocavalla]

Thank you. I am going to use your graphs in a lecture (with proper reference to your video). Great work

[@zoyamufti833]

WE NEED MORE TEACHERS LIKE YOU,SUPERB

[@43284i]

The phd students in a fancy business school really appreciate you, Rahul. Thank you!

[@5rbashat]

That's just awesome!! Thank you so much ✨

[@natkritalothongdaeng8134]

You safe my life. Thank you sir!

[@adorableanish]

2019 and thankful to you. Hope you have a great day!

[@drbniles]

30 fucking years to find this. At last.

[@anaherrera4805]

Thank you, this helped me a lot! Very good teaching

[@nurgulsaliyeva8608]

Thank you! So easy to understand!

[@yazminramos393]

Thanks to your explanation I got it! Blessings Mr Patwari ;)

[@priyabratamallick5073]

Great explanation

[@bevansmith3210]

Brilliant explanation, thank you!

[@VishalSinghVP]

Great video. Just has everything right.

[@sm11ax]

tussi great ho paaji....thanks for the video

[@hoangdaohtd]

Great explanation. Thank you.

[@silviachuquichanca7028]

Thanks a lot for the explanation!

[@tsrevo1]

Excellent. really really excellent.

[@falented2]

don't think I will ever forget this again! thanks man.. subbed!

[@psychopanda1882]

Amazing explanation Thank youuu:))

[@boezzat]

what the hell is this AWESOME lecture ! thanx

[@going_to_the_sky]

Great lecture! Thank you!

[@jre110872]

OUTSTANDING, RAHUL!!!!!

[@bishopwashington8640]

so clearly explained!

[@fatboy117]

amazing Thank you.... better lecture than Kaplan Biostats

[@Malixus3412]

Great explanation.

[@amir1010010]

Amazing! nice explanation! Thanks.

[@rajdeepbanderjee]

Thank you so much for the video.

[@Med.School.Wizards]

nice explanation of the trade-off!

[@boost4O]

Insane video. Thank you so much!

[@vwziful]

wow! bravo .I dont think there is any room for improvement .I hope , hope hope you have more videos on screening tests and biases as well.Lets see .

[@TylerdurdenFc1997]

You are a legend

[@schrodingerssmile]

Great stuff!

[@raghadpnu6221]

this is so helpful thank you so much!!!!

[@GuruprakashAcademy]

WOW GREAT EXPLANATION

[@dhiaawad6710]

man that was so helpful. thank you so much

[@nehapruthimds]

thank you !wonderful explanation.can I please ask how do we decide a cut off/ classification algorithm for a given dataset ?

[@melissacowgillhs]

Thank you very much for the video... but what about Positive predictive value and negative predictive value variations?

[@oldblueday]

Those depend on the prevalence of disease, which isn't really depicted in these histograms. I did go over that in this video, though: EBM: predictive values

[@asheikh2561]

thank u for making my life so easy

[@taraaziz2199]

So useful! thank u!

[@bonitamathew1141]

this is brilliant thank you

[@jihane859]

i finally understand it!, thank you very much!

[@phuccoiinkorea3341]

You rock!

[@tiffanymanangatheawesome950]

Thank you 😀

[@sheilahernandez5318]

Thank you soooo much!

[@fatimamohammad1439]

Thank uuuu great job

[@justinsantos9684]

Hi, i have a problem understanding the following statements, usually thrown in the context of specificity and sensitivity: A highly specific test is good for ruling in disease if positive A highly sensitive test is good for ruling out disease if negative Isnt the first statement the definition of "PPV" while the second statement is "NPV"? How do we make sense of that? Is it necessary that a highly specific test would also have a high PPV, while a highly sensitive test would have a high NPV?

[@krijgsmand77]

Is it not possible to make a test (in a perfect world) that is a 100% sensitive and a 100% specific? And in the real world? When would you choose for a more sensitive test and a more specific test?

[@douglaspierce5608]

Hello, this is great, but how come the FN is included among the TN? I see the blue shading everywhere to the left of the cutoff line, including under the FN area. Likewise on the other side of the cuoiff, the FP is included in the TP. Shouldn't the FN and the TN (and the FP and the TP) be mutually exclusive?

[@YashGumaste]

It isn't included. FN and TN combined are "test result negative".

[@YashGumaste]

TN is the part with blue shaded without slant lines, and FN is part with blue and slant lines. He should have made a clear distinction.

[@YashGumaste]

My question is how did he plot the bell shaped graph

[@oldblueday]

NEGATIVE is everything to the left of the cut-off line. If you're looking at the +DZ curve, it's a false negative. if you're looking at the -DZ curve, it's a true negative. So there will be overlap of the two since they are two different curves and the curves themselves usually overlap.

[@amethyst4444]

AMAZING VIDEO!!! THANK YOU SO MUCH!!!

[@eyeofthetiger0030]

thanks dude, good work!

[@bonsonsm]

Thank you...!

[@JK-lh9yq]

Thanx alot

[@HugueteBastida]

very nice video!! thank you

[@drbniles]

Give him a noble fking prize

[@156abdu]

Brilliant

[@aaaa-sq6xp]

awesome

[@alireza6880]

Love you

[@tgeofrey]

Wow