STA 210 Lecture Notes - Lecture 6: Null Hypothesis, Statistical Hypothesis Testing, Alternative Hypothesis
15 Apr 2017
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Document Summary
Many experiments in medicine, social science, education, etc, are designed to make a bimodal decision. A positive outcome means that the experiment has uncovered adequate evidence that the treatment is effective. A negative outcome means that the experiment has not found adequate evidence that the treatment is effective. We are generally trying to make a decision between: The process for decided between these two treatments is an example of what statisticians call hypothesis testing. This diagnostic test is not a kit or a physical examination. Rather, it consists of a collection of mathematical steps. Generally the false negative rate and the false positive rate are used to evaluate how well a screening test performs. Statistical science tends to focus on false positive rate for a similar role in hypothesis testing. Statistical tests are framed formally in terms of two competing hypotheses: Null hypothesis (ho): claim that there is no effect or difference.
