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In the context of digital forensics, event masking refers to the process of intentionally skipping the detection or consideration of certain events that may not be relevant to an investigation. This technique helps forensic analysts focus on the most pertinent data and anomalies while disregarding events that could clutter the data set or lead to unnecessary distractions.

The rationale behind event masking is to streamline the analysis process, ensuring that analysts only engage with data that could potentially inform conclusions about the investigation at hand. For instance, in a scenario where a system generates numerous logs, many of these logs may pertain to routine operations, which would not be relevant to a security incident. Event masking would allow the analyst to ignore these non-critical logs, thus refining their approach and enhancing the efficiency of the forensic analysis.

On the other hand, the concept of avoiding events that could crash a system isn't central to the main idea of event masking, as it focuses on the strategic exclusion of data rather than the operational capacity of the system itself. Hence, while system stability is important in a broader digital forensics context, it does not specifically define event masking.