What do analysts desire to determine when utilizing confidence intervals in testing?

When analysts utilize confidence intervals in testing, their primary objective is often to estimate a parameter for a population based on sample data, notably the average population score. Confidence intervals provide a range within which we can expect the true population parameter to lie with a certain level of confidence, typically 95% or 99%. This understanding helps analysts assess the likely values of population characteristics, enabling better decision-making based on the data derived from samples.

The focus is not on postulating how individuals would have scored without errors, nor is it specifically about assessing treatment effectiveness or examining variability among sample means. Instead, the goal is to gain a clearer and statistically-backed estimate of the average score across the entire population based on the sample data at hand. The interpretation of confidence intervals is key in conveying the level of uncertainty around these estimates, enhancing the robustness of analysis.