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In the context of statistical hypothesis testing, a Type I error occurs when the null hypothesis is rejected when, in fact, the null hypothesis should have been accepted. More specifically, a researcher observed a significant difference between two experimental conditions and consequently rejected the null hypothesis when, in truth, the observed significant difference between the two experimental conditions did not occur because of the manipulation, rather it occurred because of random chance. Three everyday examples of Type I errors are when a medical test indicates that a patient has a disease when, in fact, the patient is actually disease-free; when a fire alarm indicates there is a fire when, in fact, there is no fire; and when a jury decides a person is guilty of ...

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