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 Type I and Type II Error  



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Author16 Posts
  #1

What is the probability of making a type I error?
what is the prob. of making a type II error?




  #2

Which is worse?


  #3

The probability of making type I erroris alpha,P<0.05. The probability of making type II error is

beta.



  #4

The probability of making type I erroris alpha,P<0.05. The probability of making type II error is

beta.



  #5

Type I error generally represents False negative,that is as P becomes less than 0.05 the false negative are going to be decreased.In the same way in Type II error the power of the test is denoted by 1- Beta.the power represents the true negatives where as beta represents the false positives.hence a balance should be struck so that there is a balance between them.but most clinical research studies P less than 0.01.hence as cut off point is very low the alpha error is decreased but there is a chance of increased beta error,that is false pos.. are increased.and vice versa occurs.hence balace should be struck between the two.

hope Iam clear about it



  #6

I think alpha error is worse. It's like saying a drug does produce a clinically significant when in fact it does not..(Like a Lie). A type 2 error is like saying a drug does not work when it fact it does have therapeutic significance. ??? I think this is it..correct me if i'm wrong.



  #7

You all are right. The probability of making a type I Error is alpha or The probability of making a type II Error is Beta. Beta=1-Power.

Now whats the answer to this Q? and WHY???

8. A group of researchers mistakenly conclude from a poorly designed experiment that acetaminophen cures the common cold. They have committed which of the following?<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" />





A. 1-&beta; error



B. Alpha error



C. Beta error



D. Type I error



E. Type II error





  #8

Type II error,because they have would taken alpha very less hence more chance of type II error



  #9

i think alpha error


  #10

ans:type 1 error

alpha is the probability of making type one error .



  #11

The correct answer is D. Type I errors occur when researchers reject the null hypothesis when they should not have. In other words, they conclude a significant result when in actuality it does not exist. This is a particularly dangerous error to make, as it could lead to the administration of an ineffective drug to patients in need of life-preserving treatment. <?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" />

Thanks everyone.
To conclude:
Type I error is worse
alpha is the probability of making a type I Error.
Type II error = 1-Power
(Type II error (beta error) does NOT equal alpha!)



  #12

Very good Dr. Virgo, because the definitions of both may appear as "double talk" to many". But in inactuality this is a significant part of biostat and epidemiolgy.


  #13

thanks


  #14

Is Type I error the same as alpha error?
Where is this question coming from? I assume that is not from actual USMLE exam, or from NBME.

Alpha = I = False Positive (in reality it is false, but was mistakebly accepted by study as truth, a bad drug but shown as a good one = LIE)
ONLY if we reject H(0) but must not - alpha(I) error
ONLY Rejecting H(0) we could make alpha(I) error with chance 5% (5 from 100, 1 from 20) if p=0.05

Beta= II = False Negative (in reality it is truth, but was rejected, a good drug but shown as a bad one = MISTAKE)
ONLY if we accept H(0) but must not - beta(II) error
ONLY Accepting H(0) we could make beta(II) error with chance 1-Power (p=0.05 does not matter)

LIE(I) is worst then MISTAKE(II)

----------------------------------------------REALITY
----------------------------------TRUTH(+)------------FALSE(-)

STUDY SHOW TRUTH (+)---------TP-------------------FP(I)alpha

STUDY SHOW FALSE (-)-------FN(II)beta---------------TN


TP=POWER= 1 - beta



  #15

TYPE ONE ERROR,,,YOU REJECT NULL HYPOTHESIS(COZ YOU MARKETING UNEFFECTIVE DRUG),,,,GOOD LUCK IF IN EXAME NO THOUGHT BLOCK!!!!!


  #16

alfa or type 1 error is actually false positive!!





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