in order to remember stuff i need to understand their

Brooklynn Hubbard 2022-05-10 Answered
in order to remember stuff i need to understand their reason. Right now i cannot remember what is type 1 error and what is type 2 error why is the reason type 1 is false positive?
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Answers (2)

Mathias Patrick
Answered 2022-05-11 Author has 22 answers
Keeping in mind that we're discussing Neyman-Pearson type hypothesis testing, if we're going to number them (and I am not saying there's necessarily a strong reason to do that), there's a logical reason to give one of the two error types primacy:
The Type I error rate is the one you choose (when you set your significance level).
The Type II error rate is then a consequence of that choice (along with some other things like effect size and sample size).
This distinction (that the first kind of error is the one whose rate you choose while the second rate follows) seems to have been pretty much there right from the start. They (Neyman and Pearson) certainly defined the first and second types, and the NP testing framework has always had that 'choose the first rate the second follows' structure.
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studovnaem4z6
Answered 2022-05-12 Author has 7 answers
In an experiment we want to be 95% sure that our conclusion (the alternative hypothesis) is true. That leaves 5% chance that we are plain wrong. This type of error is pretty straight forward and therefore considered the first type of error that can occur (false positive). The probability is also straight forward. It is simply the 5% that we defined ourself anyway ( α).
The second type of error is more convoluted. It is the chance we are not sure enough to draw our conclusion (the alternative hypothesis), even though it is true anyway.
The second type of error is not really a false negative, since we can never draw the conclusion that the alternative hypothesis is false (i.e. negative). We can only say we do not have sufficient proof that the alternative hypothesis is true. Usually it cannot be determined either, or at least not without making precarious assumptions. As I said, it is a bit more convoluted.
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Relevant Questions

asked 2022-05-07
How the false positive value affects accuracy?
T P   ( t r u e   p o s i t i v e )   =   2739
T N   ( t r u e   n e g a t i v e )   =   103217
F P   ( f a l s e   p o s i t i v e )   =   43423
F N   ( f a l s e   n e g a t i v e )   =   5022
a c c u r a c y = T P + T N T P + T N + F P + F N
In this case the accuracy is 0.68. Can I say that I have low accuracy because the value false positive is high? There is any relathion between false positive and the parameters true positive or true negative?
asked 2022-06-05
While it makes some sense, it's not clear to me why those are different. If a test, say medical test, is correct 90% of time then chances of it being wrong is 10%.
But I've read things that say in medical field test with high accuracy for negatives are used as screening method and more expensive tests which are high accuracy positive tests are used only when former tests come positive. Because if former came negative we are confident patient doesn't have a disease but if it comes positive we are not as sure, thus we do expensive test. Which of course, makes sense.
I get there are 4 events:
1. Test is +, patient has a disease
2. Test is -, patient doesn't have a disease
3. Test is +, patient doesn't have a disease
4. Test is -, patient has a disease
asked 2022-06-13
Given that out of 1000 individuals where 60 use a drug. I am given the probability of a false positive is 0.009 and the probability of a false negative is 0.10. I'm trying to find the true positive and true negative.
Let D be the even that the user uses a drug and D C the event the user is not a drug user. I know that P ( D ) = 0.06 and P ( D C ) = 0.994 as well as P ( + | D c ) = 0.009 and P ( | D ) = 0.10. So that means that P ( + | D C ) = 0.009 0.009 990 = 8.91 people. Also P ( | D ) = 0.10 60 .10 = 6 people. Does it follow that P ( | D C ) = 981.09 / 990 = 0.991 and P ( + | D ) = 54 / 60 = 0.90?
asked 2022-06-08
if 1:1000 of people is sick. the probability to be false positive is 0.07. if a person is sick there is not chance the test for the disease is wrong. If someone random is got a positive result, what are the chances he's actually sick?
I got 1.5% and wanted to check because I feel it should be more since the diagnose is never wrong. I took 1/1000 and divided it by 0.07+1/1000 and multiplied by 100 to get the percent.
asked 2022-05-01
The latest worldwide virus has an infection rate of 0.1 % (that is, 1 in 1000 people actually have the virus). The chance that someone who has the virus tests positive is said to be 99 %. The chance that someone who does not have the virus tests negative is also said to be 99 %. What are the chances that someone who tests positive does not in fact have the virus (a “false positive”)?
asked 2022-04-07
The prevalence of breast cancer in women over 40 in country X is estimated to be 0.8% (i.e., 8 in every 1,000 women in that age group).
Mammograms test for the presence of breast cancer. A positive result indicates that the disease is present. A negative result indicates that it is not.
The sensitivity of a mammogram test for breast cancer is estimated to be 90%. This is the probability that the mammogram will give a positive result when the person being tested does have breast cancer.
The false positive rate for the mammogram is 7.5%. This is the probability that the mammogram will give a positive test result when the person being tested does not have breast cancer.
All women who test positive (816) in the mammogram are referred for a further, different examination, which however has the same sensitivity and false positive rates as the first test.
What is the probability that a woman referred for this examination and testing positive again, actually does have breast cancer?
asked 2022-05-08
My problem is the following. I like to know if there exist a sentence true in complex a field but false in a field of positive characteristic.