A range of approaches have been proposed for estimating the accuracy or robustness of the measured performance of IR methods. One is to use bootstrapping of test sets, which, as we confirm, provides an estimate of variation in performance. For IR methods that rely on a seed, such as those that involve machine learning, another approach is to use a random set of seeds to examine performance variation. Using three different IR tasks we have used such randomness to examine a range of traditional statistical learning models and transformer-based learning models. While the statistical models are stable, the transformer models show huge variation as seeds are changed. In 9 of 11 cases the F1-scores (in the range 0.0--1.0) had a standard deviation of over 0.075; while 7 of 11 precision values (also in the range 0.0--1.0) had a standard deviation of over 0.125. This is in a context where differences of less than 0.02 have been used as evidence of method improvement. Our findings highlight the vulnerability of transformer models to training instabilities and moreover raise questions about the reliability of previous results, thus underscoring the need for rigorous evaluation practices.