Recent studies have suggested that genes longer than 100 kb are more likely to be misregulated in neurological diseases associated with synaptic dysfunction, such as autism and Rett syndrome. These length-dependent transcriptional changes are modest in MeCP2-mutant samples, but, given the low sensitivity of high-throughput transcriptome profiling technology, here we re-evaluate the statistical significance of these results. We find that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-sequencing datasets disappear after estimating baseline variability from randomized control samples. This is particularly true for genes with low fold changes. We find no bias with NanoString technology, so this long gene bias seems to be particular to polymerase chain reaction amplification-based platforms. In contrast, authentic long gene effects, such as those caused by topoisomerase inhibition, can be detected even after adjustment for baseline variability. We conclude that accurate characterization of length-dependent (or other) trends requires establishing a baseline from randomized control samples.