Which term best describes the computation involving (mean1 - mean2)/standard deviation?

The term that best describes the computation involving ((\text{mean1} - \text{mean2})/\text{standard deviation}) is effect size. This calculation essentially measures the magnitude of the difference between two means relative to the variability observed in the data, which is necessary for understanding the practical significance of the difference.

Effect size is crucial in research because it provides context to statistical findings beyond mere significance testing. While p-values can indicate whether an effect exists, effect size quantifies how large that effect is. This is particularly useful when comparing results across studies or when determining the robustness of findings.

In contrast, a Z score represents how many standard deviations an element is from the mean within a single dataset, focusing on individual data points rather than differences between groups. Type 1 error refers to incorrectly rejecting a true null hypothesis, and statistical power is the probability that a test will correctly reject a false null hypothesis. These concepts, while important in statistics, do not describe the computation involving the means and standard deviation in the same way effect size does.