In statistical hypothesis testing, selecting an appropriate significance level () is an essential part of designing research because it reflects the researcher's tolerance for Type I error (false positives). The conventional -level of 0.05 is widely used across disciplines, but there are logical reasons to choose more or less stringent levels depending on the context of the research (e.g., clinical outcomes vs. exploratorystudies).

A lower -level (e.g., 0.01) would be more appropriate in scenarios where the consequences of false positives could be severe, costly, or harmful to patients. For example, in clinical drug trials, approving an ineffective drug due to a false positive could exposepatients to unnecessary side effects and delay better treatment options. In these cases, reducing the risk of erroneously rejecting the null hypothesis is critical. Choosing =0.01 reduces the probability of a Type I error and demands stronger evidence beforeconcluding that an effect exists. This results in more confidence in findings where safety and accuracy matter most.

In contrast, a higher -level (e.g., 0.10 or 0.20) may be acceptable in exploratory research or early-stage studies where researchers prioritize observing potential effects and generating hypotheses for further study. Here, the cost of missing a true effect (Type II error) may outweigh the cost of a false positive. A more lenient significance level increases the chance of detecting a real effect, which can be valuable when it is more important to identify promising leads than to definitively confirm them.

References:

Kim, T. K. (2015).T test as a parametric statistic.Korean Journal of Anesthesiology, 68(6), 540-546.https://doi.org/10.4097/kjae.2015.68.6.540 Henderson, D., & Mulkey, D. (2024).Applied statistics for health care.Grand Canyon University.

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