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A type I error, also known as an error of the first kind, occurs when the null hypothesis (H0) is
true, but is rejected. It is asserting something that is absent, a false hit. A type I error may be
compared with a so-called false positive (a result that indicates that a given condition is present
when it actually is not present) in tests where a single condition is tested for. Type I errors are
philosophically a focus of skepticism and Occam's razor. A Type I error occurs when we believe
a falsehood.[4] In terms of folk tales, an investigator may be "crying wolf" without a wolf in
sight (raising a false alarm) (H0: no wolf).
The rate of the type I error is called the size of the test and denoted by the Greek letter ? (alpha).
It usually equals the significance level of a test. In the case of a simple null hypothesis ? is the
probability of a type I error. If the null hypothesis is composite, ? is the maximum (supremum)
of the possible probabilities of a type I error.
A false positive error, or in short false positive, commonly called a "false alarm", is a result
that indicates a given condition has been fulfilled, when it actually has not been fulfilled. I.e.
erroneously a positive effect has been assumed. In the case of "crying wolf"
Solution
A type I error, also known as an error of the first kind, occurs when the null hypothesis (H0) is
true, but is rejected. It is asserting something that is absent, a false hit. A type I error may be
compared with a so-called false positive (a result that indicates that a given condition is present
when it actually is not present) in tests where a single condition is tested for. Type I errors are
philosophically a focus of skepticism and Occam's razor. A Type I error occurs when we believe
a falsehood.[4] In terms of folk tales, an investigator may be "crying wolf" without a wolf in
sight (raising a false alarm) (H0: no wolf).
The rate of the type I error is called the size of the test and denoted by the Greek letter ? (alpha).
It usually equals the significance level of a test. In the case of a simple null hypothesis ? is the
probability of a type I error. If the null hypothesis is composite, ? is the maximum (supremum)
of the possible probabilities of a type I error.
A false positive error, or in short false positive, commonly called a "false alarm", is a result
that indicates a given condition has been fulfilled, when it actually has not been fulfilled. I.e.
erroneously a positive effect has been assumed. In the case of "crying wolf"

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  • 1. A type I error, also known as an error of the first kind, occurs when the null hypothesis (H0) is true, but is rejected. It is asserting something that is absent, a false hit. A type I error may be compared with a so-called false positive (a result that indicates that a given condition is present when it actually is not present) in tests where a single condition is tested for. Type I errors are philosophically a focus of skepticism and Occam's razor. A Type I error occurs when we believe a falsehood.[4] In terms of folk tales, an investigator may be "crying wolf" without a wolf in sight (raising a false alarm) (H0: no wolf). The rate of the type I error is called the size of the test and denoted by the Greek letter ? (alpha). It usually equals the significance level of a test. In the case of a simple null hypothesis ? is the probability of a type I error. If the null hypothesis is composite, ? is the maximum (supremum) of the possible probabilities of a type I error. A false positive error, or in short false positive, commonly called a "false alarm", is a result that indicates a given condition has been fulfilled, when it actually has not been fulfilled. I.e. erroneously a positive effect has been assumed. In the case of "crying wolf" Solution A type I error, also known as an error of the first kind, occurs when the null hypothesis (H0) is true, but is rejected. It is asserting something that is absent, a false hit. A type I error may be compared with a so-called false positive (a result that indicates that a given condition is present when it actually is not present) in tests where a single condition is tested for. Type I errors are philosophically a focus of skepticism and Occam's razor. A Type I error occurs when we believe a falsehood.[4] In terms of folk tales, an investigator may be "crying wolf" without a wolf in sight (raising a false alarm) (H0: no wolf). The rate of the type I error is called the size of the test and denoted by the Greek letter ? (alpha). It usually equals the significance level of a test. In the case of a simple null hypothesis ? is the probability of a type I error. If the null hypothesis is composite, ? is the maximum (supremum) of the possible probabilities of a type I error. A false positive error, or in short false positive, commonly called a "false alarm", is a result that indicates a given condition has been fulfilled, when it actually has not been fulfilled. I.e. erroneously a positive effect has been assumed. In the case of "crying wolf"