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

Рет қаралды 10,963

6 ай бұрын

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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Пікірлер: 26

@Rosh12373 6 ай бұрын

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 5 ай бұрын

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 6 ай бұрын

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 5 ай бұрын

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

@jaykishan3633 2 ай бұрын

SuPPerbbb explained Sir...

@ZibaFact 6 ай бұрын

Thanks 😊👍🏻

@user-hl9tc9qg3w 3 ай бұрын

Thank you sir

@physics3603 28 күн бұрын

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

@SaurabhSingh-sy1pe 23 күн бұрын

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

@user-nm8fb1wk6d 6 ай бұрын

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

@baithak3438 3 ай бұрын

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

@sagardeepsingh5151 Ай бұрын

true

@hsk7715 5 ай бұрын

Confusion matrix kafi confusing hey

@rajkumardubey5486 6 ай бұрын

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 6 ай бұрын

Good question

@nayeemx11 2 ай бұрын

please complete full statistics for DS/ML

@youtubeshorter5297 21 күн бұрын

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

@sanch6239 5 ай бұрын

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 5 ай бұрын

Yes also I think so

@OBECEOODishantKorat 4 ай бұрын

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