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A type 2 error is when you accept your null hypothesis when in fact the alternative is true. A type 2 error is also known as a false negative.

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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

diabetes are two type 1insulin dependent diabetes 2 non insulin dependent diabetes

That depnds on the study

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

No....the two are mirror images of each other. Reducing type I would increase type II

It can have bad consequences either way, depending on the subject of the study.

2%

when we declare an array then we declare it as follows: data type array[size] here , 1)The size should be assign a value 2) That value should be an integer If we give the value as float then the (1) criteria is satisfied but the (2) creteria is not yet satisfied, thus it creates an error. Such type of error are refered as SEMANTIC ERRORS.

1. Making many determinations of the physical properties. 2. Performing many chemical reactions to study the chemical properties of NaCl.

A: The last time that i kept up with types of systems there were only three what is along?. Well anyhow type-0 system is one that requires a constant error signal to operate type-1 a constant rate of change of the controlled variable requires a constant error signal under steady state condition. type 1 is usually referred as servomechanism system. type-2 a constant acceleration of the control variable requires a constant error under steady state condition. type-2 sometimes is referred to as zero velocity error system

Hitler Germany fighting n Poland type error: why did ww2 happen?

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basically according to performance three type or NPA 1 substandred 2 doutful 3 loss

There are type 1 and type 2 errors in studies. Type 1 errors are an incorrect rejection of a certain hypothesis. An example is incorrectly diagnosing someone with an illness.

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The error in its area is then 2 percent....

area= side^2 let the symbol # denote error in measurement #area/area= 2(#length/length) #area/area*100= 2(#length/length)*100 percent error in area= 2*percent error in length=2% 2 per cent

depends on the consecence of make the mistake sometimes one is worse then 2 and sometimes its the other way round

This is when you reject a null hypothesis even though it is actually true...Example:1. A man is on trial for murder, he is actually INNOCENT, but found GUILTY - That is a Type I error2. A man is on trial for murder he is actually GUILTY, but found INNOCENT - That is a Type II error

Compress the chest for an adult when performing chest compressions 1 1/2 to 2 inches

1/100

The feasibility study has 2 components:1. Feasibility Study Request2. Feasibility Study Report

Old type, analog power factor meters may be considered 2% instruments.

Operator error.