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The significance level typically used in hypothesis testing is 0.05. This level indicates the probability of rejecting the null hypothesis when it is, in fact, true (Type I error). By convention, a 0.05 significance level means that there is a 5% risk of concluding that a difference exists when there is no actual difference.

Using a significance level of 0.05 is standard in various fields because it strikes a balance between being too lenient (leading to more false positives) and too strict (potentially missing true effects). Researchers often choose 0.05 as it provides a reasonable threshold for making decisions about the validity of their hypotheses without being overly cautious.

In some instances, other significance levels such as 0.01 or 0.10 may be used depending on the context of the study and the risks associated with Type I errors. For example, a 0.01 level might be more appropriate in medical trials where the consequences of false positives could be more severe. However, 0.05 remains the most common choice across many research disciplines, making it a fundamental aspect of hypothesis testing.