What are three key statistical terms you encounter when monitoring A/B test results in Google Ads?

• A. Confidence level, confidence interval, and statistical significance
• B. P-value, z-score, and beta
• C. Margin of error, sample size, and power
• D. Means, medians, and modes

Answer: (A)

When monitoring A/B test results, you want to quantify how sure you are about the observed difference and whether it’s likely real or just random variation. The three terms you’d focus on are confidence level, confidence interval, and statistical significance. The confidence level shows how sure you want to be about your interval estimate (for example, 95% means you expect the true effect to lie within the interval 95% of the time in repeated experiments). The confidence interval provides a range of plausible values for the true effect size based on the data you collected. Statistical significance tells you whether the observed difference is unlikely under the assumption there is no true difference, helping you decide if the result should be acted upon.

P-value, z-score, and beta belong to significance testing and regression contexts more broadly, but aren’t the standard trio used to interpret A/B test outcomes in Google Ads dashboards. Margin of error, sample size, and power are important for planning experiments and precision, not the immediate interpretation of monitored results. Means, medians, and modes are basic descriptive stats, not the focused inference terms used for deciding whether to implement a change.