Missing a data point because a weighing scale ran out of batteries unexpectedly is an example of ________.

The situation described—where a data point is missing due to an unexpected event such as a weighing scale running out of batteries—illustrates the concept of Missing Completely at Random (MCAR). In MCAR, the probability of missing data on a variable is entirely independent of both the observed and unobserved data. This means that the missingness is not related to any specific characteristics of the data itself or any underlying patterns in the dataset.

In this case, the failure of the weighing scale is an unforeseen incident that does not correlate with the attributes of the individuals being weighed or the circumstances around the data collection. Therefore, the missing data isn't influenced by factors that could bias the results, genuinely representing randomness in the data collection process.

This contrasts with situations where data is missing due to systematic reasons, which would lead to different implications for analysis and potential bias if, for example, certain groups are consistently underrepresented. Understanding the type of missing data is crucial, as it informs appropriate methods for handling the missingness and the robustness of any conclusions drawn from the analysis.