Dismental the calculator and press type 1 error there you got it( for any calculator
In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.
type1 error is more dangerous
For a relative error maybe it is: (Vout_hi - Vout_lo) / (Vout_hi_nom - Vout_lo_nom) - 1
This will reduce the type 1 error. Since type 1 error is rejecting the null hypothesis when it is true, decreasing alpha (or p value) decreases the risk of rejecting the null hypothesis.
In statistics: type 1 error is when you reject the null hypothesis but it is actually true. Type 2 is when you fail to reject the null hypothesis but it is actually false. Statistical DecisionTrue State of the Null HypothesisH0 TrueH0 FalseReject H0Type I errorCorrectDo not Reject H0CorrectType II error
Type I error happens when a difference is being observed when in truth, there is none or there is no statistically significant difference. This error is also known as false positive.
(0.6745 * Standard deviation)/ (n^1/2) :)
diabetes are two type 1insulin dependent diabetes 2 non insulin dependent diabetes
A combination of factors increase the risk of a Type 1 error. Giving the wrong amount or wrong diagnosis for a wrong drug would certainly increase an error.
The power of a test is 1 minus the probability of a Type II error.
No....the two are mirror images of each other. Reducing type I would increase type II
1) to calculate coeficient of error 2) to calculate deviation between the readings to infere a behaviour