What does the alpha-level signify before conducting statistical tests?

The alpha-level is a critical concept in statistical testing, representing the threshold for determining whether a result is statistically significant. It is typically set at a value like 0.05, meaning that there is a 5% chance of rejecting the null hypothesis when it is true (Type I error). When researchers conduct statistical tests, they compare the p-value, which reflects the strength of evidence against the null hypothesis, to the alpha-level. If the p-value is less than or equal to the alpha-level, the null hypothesis is rejected, leading to the conclusion that the results are statistically significant.

This idea of the alpha-level being a statistically significant threshold establishes the foundation for hypothesis testing, allowing researchers to differentiate between results due to random chance and those that indicate a true effect or association in the data. By setting this threshold before conducting the test, researchers maintain control over the likelihood of making incorrect inferences based on the sample data.