The SSRT is an estimate of stopping or inhibition speed and is derived by subtracting from a measure of “go” RT, a measure of the stop-signal delay –the stimulus-onset asynchrony between “go” and “stop” stimuli. However, SSD is determined differently across studies and can take the form of a single fixed SSD, average of multiple fixed SSDs, or SSD tracking. Even when the same SSD is used, there are differences in the way in which the SSRT is computed. An estimate commonly used is the SSRT central, computed at the central SSD where the race between “go” and “stop” processes ends in a tie and the success/failure rate of inhibition is 50%. The central SSD is often estimated with a tracking algorithm that dynamically adjusts the SSD according to performance on the previous “stop” trial. That is, following each (+)-JQ1 successful “stop”, the likelihood of successful inhibition at the next “stop” trial would be decreased by delaying the onset of the stop-signal. SSRTcentral is reportedly the most accurate and reliable estimate of stop-signal inhibitory efficiency when achieved response rates are around 50%. However, it can over-estimate SSRT when response rates deviate from 50%, for example, when participants engage in strategic response slowing in anticipation of the “stop” stimuli, or when the RT distribution is positively skewed. In this case, computing SSRT using the integration method has been argued to be more robust as it takes into account the actual response rate achieved. This method involves rank-ordering “go” RTs and subtracting the SSD at the actual achieved response rate from the “go” RT value at the percentile corresponding to the achieved response rate. However, SSRTintegration tends to be underestimated when there is gradual response slowing over trials. In the case that subjects exhibiting slowing cannot be removed from analysis, SSRTintegration can be calculated as an average over smaller blocks of trials to yield a more accurate estimation of SSRT. Less widely used measures of stop-signal inhibition include commission errors, probability of inhibition, and the inhibition function curve. Such measures are however, limited to paradigms that employ fixed stop-signal delays as they will be artificially influenced by tracking algorithms. Because different measures may emphasize the influence of different processes underlying the Stroop or stop-signal task, different findings can be expected across studies that used different measures. To our knowledge, only one study has specifically examined how variations in calculating SSRT can affect its relationship with other measures. None has compared variations in calculating Stroop interference measures. It is an aim of the present study to examine if inconsistent findings on the relationship between Stroop and stop-signal measures may be due in part to variations in how dependent measures were derived. The present study explored the relationship between Stroop and stop-signal inhibition using a variety of derived measures. In a previous study, we examined the relationship amongst six inhibitory tasks and how they predicted algebra word problem solving performance in young adolescents.