In hypothesis testing, a significance level is commonly denoted by which symbol?

In hypothesis testing, the significance level is commonly denoted by the symbol α (alpha). This significance level represents the threshold for determining whether to reject the null hypothesis. It is set before conducting the test and typically takes values such as 0.05 or 0.01, indicating a 5% or 1% risk of concluding that a difference exists when there is none (type I error).

Understanding the significance level is crucial because it helps researchers decide how strong the evidence must be to reject the null hypothesis. A lower significance level indicates a more stringent criterion for rejecting the null, while a higher level allows for a greater chance of detecting an effect if one exists.

The other symbols listed serve different purposes in statistics. For instance, p represents the p-value, which is the probability of obtaining a test statistic as extreme as the one observed, assuming the null hypothesis is true. β (beta) denotes the probability of a type II error, which occurs when the null hypothesis is not rejected when it is false. μ (mu) is the symbol for the population mean. Therefore, the designation of α as the significance level is a foundational concept in hypothesis testing, essential for appropriately interpreting the results of statistical analyses.