Type I error is the incorrect rejection of the null hypothesis, influenced by the significance level and alpha values.
Type I error, symbolized by alpha, is the incorrect rejection of the null hypothesis in hypothesis testing, leading to a false positive outcome. For example, claiming there is enough statistical support for a research hypothesis when there is not constitutes a Type I error.
Alpha level (a) is the significance level that defines the probability of committing a Type I error. By setting a higher alpha level, the risk of Type I error decreases. In public health and clinical research, an alpha level of 0.05 is commonly accepted.
Power in statistical testing refers to the ability to correctly detect an effect that exists. It is influenced by the size of the effect, significance level, and sample size used.
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