Sensor networks aim at monitoring their surroundings for event detection and object tracking. In this talk, we consider the problems of distributed fault detection in wireless sensor network(WSN). We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process. We introduce two types of detection probabilities, one for the center node, where the event occurs, and the other one for the adjacent nodes. We develop schemes under the statistical model selection procedure and multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error. We show how prediction of events can improve using statistical hypothesis testing with multiple alternatives.