An analysis of the significance of type I and type II errors in nursing- based statistical analysis.
Written in 2009; 859 words; 4 sources; APA; $ 30.95
Paper Summary:
Two kinds of errors can occur in significance testing. These are type I and type II errors. This paper provides an exploration of type I and type II errors and looks at how to determine if they occur as well as how they can be prevented. The paper attempts to show that the prevention of these errors will allow students and researchers of nursing to perform more accurate hypothesis tests, as well as help them understand the reliability of statistical testing.
From the Paper:
"A Type II error occurs when a false null hypothesis is not rejected. In other words, the false statement that there is no relationship between the variables has failed to be rejected. This case is not as serious as a Type I error for the simple statistical rule that causation does not equal correlation. Because rejecting the null hypothesis does not suggest that a relationship exists between the variables, but only that the null hypothesis is incorrect, a failure to reject the null hypothesis makes no conclusions (Lane 2008). The difference between Type I and Type II errors in terms of seriousness, therefore, is rather pronounced. A Type I error is seen as a serious error because it is stating that something is true--that the null hypothesis is false when it is actually true--while a Type II error makes no such claims, but only results in a failure to reject the null hypothesis. "
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