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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

Q: Type 1 error and type 2 error?

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type1 error is more dangerous

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

If two typists type 2 pages in 5 minutesIn 1 minute they will type 2/5pagestherefore 2 typists type 2/5 pages in 1 minute1 typist will type (2/5)/2 pages in 1 minute(he will type less)=1/5 pages.... eqn 1x typists type 20 pages in 10 minutesin 1 minute they will type 20/10 pages=2 pagestherefore x typists type 2 pages in 1 minute1 typist will type 2/x pages............eqn 2equating eqn 1 and 2 gives 2/x=1/5therefore x =10 typists

A type 1 error (alpha) is when a statistic calls for the rejection of a null hypothesis which is factually true. This is unavoidable: statistically speaking, even purely random events will occasionally produce non-random seeming results. A type 1 error is called a false positive because it forces the researcher to make a 'positive' statement that something odd is happening, when in fact nothing is happening except an excess of chance (in the same way that your family doctor means that something odd is going on with your body when she tells you that a test came back positive). A type 2 error (beta) is when a statistic does not give enough evidence to reject a null hypothesis even when the null hypothesis should factually be rejected. Increasing the power of the test (by increasing sample size, or making new assumptions) can help to minimize the likelihood of this error. A type 2 error is a false negative, for the same reasons as above - the researcher is forced to say that nothing interesting is happening.

1/2

Related questions

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

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

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.

2%

I believe that Type 1 is an autoimmune disease and Type 2 is not, so with Type 1 auto antibodies are present and with Type 2 they are not. Therefore, I do not believe that taking insulin can turn Type 2 into Type 1. The cause of Type 1 is still unknown.

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

type 1.

1/2 (type a 1, then a /, then a 2)

Type 1.

Type 2.