What is the alpha-level defined as in hypothesis testing?

The alpha-level in hypothesis testing is a critical threshold that defines the likelihood of making a Type I error, which occurs when a null hypothesis is incorrectly rejected. It represents the probability of incorrectly concluding that a treatment or effect exists when, in reality, there is none. Typically set at 0.05, this alpha-level is used to determine the cutoff for statistically significant results; if the p-value obtained from the study is less than the alpha-level, the null hypothesis can be rejected.

While other options relate to aspects of statistical analysis, they do not accurately capture the definition of the alpha-level. The p-value indicates the strength of evidence against the null hypothesis, but it is not the same as the alpha-level. The standard deviation measures the variability within a dataset, which is unrelated to the concept of alpha in hypothesis testing. Finally, the term 'statistically significant' refers to a result that falls below the alpha-level threshold but does not define what the alpha-level is. Therefore, the alpha-level is characterized as the error of inference, highlighting its role in controlling the rate of false positives in hypothesis testing.