Motivated by recent critique toward partial least squares path modeling (PLS), we present a research question if the PLS method, as used currently, is at all an appropriate tool for theory testing. We briefly summarize some of the recent critique of the use of PLS in IS as a theory testing tool. Then we analyze the results of 12 PLS analyzes published in leading IS journals testing if these models would have been rejected in the case that the data used for model testing had very little correspondence with the theorized models. Our Monte Carlo simulation shows that PLS will often provide results that support the tested hypotheses even if the model was not appropriate for the data. We conclude that the current practices of PLS studies have likely resulted in publishing research where the results are likely false and suggest that more attention should be paid on the assumptions of the PLS model or that alternative approached like summed scales and regression or structural equation modeling with estimators that have known statistical properties should be used instead.