Tutorial 19- Type 1 And Type 2 Error In Statistics-Krish Naik Hindi

In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion.
Making a statistical decision always involves uncertainties, so the risks of making these errors are unavoidable in hypothesis testing.
The probability of making a Type I error is the significance level, or alpha (α), while the probability of making a Type II error is beta (β). These risks can be minimized through careful planning in your study design.
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[@Rosh12373]

Type 1 : occurs when the null hypothesis is rejected when it is true
Type 2 : occurs the null hypothesis is accepted when it is false

[@harmeeksingh2320]

Sir you have no idea how good you are as a teacher, this playlist has really helped me to learn a lot about the Applied Statistical Analysis.
I request you to please complete this playlist.
Thank you very much.
Keep up the good work.

[@VickyPatel1293]

You can easily understand this concept by considering an example...
Let's assume we are assessing whether a patient has cancer or not.
If our model predicts a positive result, but in reality, the patient does not have cancer, it is a false positive (Type 1 error), which is not harmful in the case of cancer.
However, if the model predicts a negative result, and in reality, the patient has cancer, it is a false negative (Type 2 error), which is very detrimental to our model.
Therefore, our focus should be on reducing this specific misclassification error.

[@AkashSharma-ij4sn]

this is understandable but the way he created the relation with confusion matrix to conclude type1 and type2 error thats not clear

[@baithak3438]

Your FP and FN need to reverse in confusion matrix . They r causing real confusion .

[@sagardeepsingh5151]

true

[@physics3603]

Please sir help me to solve this confusion or some one please please explain if I am wrong
3:31 yaha pr aapne kaha he ki , FP(i.e predicted true but in actual it is false) is type 1 error and FN(i.e predicted False but in actual it is true) is type 2 but here 12:28 , you say that we rejected the null hypothesis but in actual it is true that means it becomes FN sir , so It should be type 2 error but you say that it is type 1 error

[@jaykishan3633]

SuPPerbbb explained Sir...

[@ZibaFact]

Thanks 😊👍🏻

[@SaurabhSingh-sy1pe]

@12:15 it will be type 2 error or False Positive
@13:59 it will be type 1 error of False Negative

[@user-hl9tc9qg3w]

Thank you sir

[@hsk7715]

Confusion matrix kafi confusing hey

[@youtubeshorter5297]

Brother 1 thing is wrong that you consider (0)of actual instead of predicted so basically outcome 3 should be FALSE NEGATIVE

[@user-nm8fb1wk6d]

Hello Krish after completion of this course will you help us in interview process also

[@nayeemx11]

please complete full statistics for DS/ML

[@sanch6239]

Krish sir, I think "Outcome 3 = FN(Type 2 Error)", & "Outcome 4=FP(Type 1 Error)". But in video you said vice versa. Please check it once again.

[@suronitachandra5791]

Yes also I think so

[@OBECEOODishantKorat]

yes, you are right. he made a little mistake.
Type 1 : occurs when the null hypothesis is accepted when it is false
Type 2 : occurs the null hypothesis is false when it is true

[@rajkumardubey5486]

To reduce this type 1 and type 2 errors is there any other way to reduce it other then hyperparameter tuning, threshold value adjustment

[@ZibaFact]

Good question

[@ashushukla8879]

in 12:05 you said in prediction it is false(0) and in reality it is true(1) then it should be FN but you take it as FP. please clarify

[@kumarkirti4438]

Yes it should be FN

[@SumanSadhukhan-md4dq]

Yes you are right it should be FN

[@punitbhadayuia]

Type 1 and type 2 error in your confusion matrix diagram is interchanged. Please edit and do correction. This decreases our confidence in your videos

[@alopsingh4772]

dimang ki upar se gya

[@VickyPatel1293]

mera comment refer karo shayad aapko help karega confusion matrix samajh ne me