Type I and II Errors

Type I and II Errors - err in the opposite way, too; you...

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Type I and II Errors You have been using probability to decide whether a statistical test provides evidence for or against  your predictions. If the likelihood of obtaining a given test statistic from the population is very small,  you reject the null hypothesis and say that you have supported your hunch that the sample you are  testing is different from the population. But you could be wrong. Even if you choose a probability level of 5 percent, that means there is a 5  percent chance, or 1 in 20, that you rejected the null hypothesis when it was, in fact, correct. You can 
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Unformatted text preview: err in the opposite way, too; you might fail to reject the null hypothesis when it is, in fact, incorrect. These two errors are called Type I and Type II, respectively. Table 1 presents the four possible outcomes of any hypothesis test based on (1) whether the null hypothesis was accepted or rejected and (2) whether the null hypothesis was true in reality. Table 1. Types of Statistical Errors H is actually: True False Reject H Type I error Correct Accept H Correct Type II error...
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