Rerandomization is a new technique employed in randomized field experiments (RFEs) to enhance the balance of covariates across experimental groups. Rerandomization is recommended because it reduces imbalances in known covariates, thereby enhancing the precision of the average treatment effect estimates. However, employing rerandomization necessitates adjusting observed p-values, using regression-based inference, and selecting predictive covariates. All these amendments increase the number of researcher degrees of freedom, i.e., methodological decisions involved in designing and analyzing experiments. I argue that this increased analytical flexibility may be misused to p-hack for statistically significant or preferred results, thereby reducing the credibility of the results.