Across many diverse areas of research, it is common to average a series of observations, and to use these averages in subsequent analyses. Research using this approach faces the challenge of knowing when these averages are stable. Meaning, to what extent do these averages change when additional observations are included? Using averages that are not stable introduces a great deal of error into any analysis. The current research develops a tool, implemented in R, to assess when averages are stable. Using a sequential sampling approach, it determines how many observations are needed before additional observations would no longer meaningfully change an average. The utility of this tool is illustrated in the context of impression formation, demonstrating that averages of some perceived traits (e.g., happy) stabilize with fewer observations than others (e.g., assertive). A tutorial regarding how to utilize this tool in researchers’ own data is provided.
Probability of Failure Analysis and Design Using An Efficient Sequential Sampling Approach
