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Outliers or anomalies are generally considered to be those observations that are considerably diverged from normal pattern of data. Due to their special characteristics, e.g. constrained available resources, frequent physical failure, and often harsh deployment area, wireless sensor networks (WSNs) are more likely to generate outliers compared to their other wireless counterparts. Potential sources of deviated data in a series of measurements are errors, events, and/or malicious attacks on the network. Current studies tend to handle events and errors separately and propose different techniques for event detection as for outlier detection. By bringing the concept of outlier and event close together and assuming that events are some sorts of outliers, in this paper, we investigate applicability of pattern matching-based event detection techniques for outlier detection. Through extensive experiments, we evaluate performance of various event detection techniques to detect outliers and compare them with a recent outlier detection study.