Western Governors University (WGU) MGMT6010 C207 Data Driven Decision Making Practice Exam

The term that refers to a number indicating the probability threshold for statistical significance is the alpha level. In statistical hypothesis testing, the alpha level (commonly set at 0.05) defines the threshold at which you reject the null hypothesis. It represents the probability of making a Type I error, which is the error of concluding that there is a significant effect or difference when none exists. By setting this threshold, researchers can determine how unlikely their observed data would be under the assumption that the null hypothesis is true.

The alpha level is critical because it helps establish a clear criterion for what is considered statistically significant. If the p-value of the test is less than or equal to the alpha level, the results are considered statistically significant, indicating that the observed effect is unlikely to have occurred by random chance alone. This is essential for making informed decisions based on statistical analysis.

Understanding the alpha level is key to interpreting results in research and data analysis, allowing researchers to make valid inferences from their data while controlling for the risk of false positives.