The use of functional magnetic resonance imaging (fMRI) has been increasing in popularity due to its ability to measure brain activity during presentation of stimuli. Blood flow responses in the brain occur when a stimulus is presented and can be measured using fMRI. The delay of onset of this blood flow response can vary due to distances from the heart to the brain blood vessels. This variability causes differences in onset and time to peak blood flow response across the brain that is not currently predictable. To account for this, statistical analyses add the response's temporal derivative to regression models. Derived from the Taylor series expansion, the temporal derivative corrects for small variations in the time delay for the blood flow response (i.e. less than 1 second or so). However, I show that inclusion of the temporal derivative in analyses increases false positive rates. I conducted fMRI analyses on data collected as participants complete motor responses and on resting state data. Analyses were repeated both with and without inclusion of the temporal derivative. More significant responses were found with inclusion of the temporal derivative in both cases, suggesting possible increases in false positive rates. The goal of the present study is to increase awareness of the current fMRI data analysis practices and their potential flaws.