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\".