Previous studies present the mixed results on online reputation mechanism. In this study, we have found that an approach based on Bayesian statistics can explain most results of previous studies which are conflicting with each others. With this model, we explain why negative ratings have more significant marginal impacts on sellers' reputation than positive ones do. Furthermore, we even show why the feedbacks with a few negative ratings may increase the value of the item and final prices by confirming buyers' prior beliefs on the sellers' reputation much more than those without negative ratings. Also, we explain why there are not many negative ratings. Even though some studies suggest this because of generosity of users, our model shows that the reason is that the existence of FS itself prevents bad sellers from participating to the market as a signal itself. Even further, we show how this extreme tendency of positive ratings gets even stronger as markets evolve. Finally, to validate our analytical results, we examine the previous studies and see what factors effect the outcomes of their analyses.