In this post I will briefly discuss a way to deal with speed-accuracy trade offs in response times experiments (RT). When conducting RT experiments and collecting responses such as correct and incorrect responses to visual stimuli one can at times find that under certain conditions people respond slower but more accurate. For instance if you have a condition with distractors and people are responding slower everything may seem fine. However, if you look at the accuracy data (proportion of correct responses) you may see that people responded faster. The inverse efficiency score combines speed and error. IES is suggested to be an “observable measure that gauges the average energy consumed by the system over time”. It is calculated by dividing RT by 1 – the proportion of Errors (PE), or the proportion of correct responses (PC). If two conditions have the same mean RT but differ in PE, IES of the condition that has the highest PE will increase more than the IES of the condition with the lower PE. Interestingly, if there is a speed and accuracy trade-off, the IES will even out the PE differences. It is not always better to use IES. Seemingly, a lot of changes can happen when using IES. This is because it includes two variables and their sampling error. Therefore, the variability of the measure increases. Furthermore, whether the division RT by PC is a good reflection of the relative weights of speed and accuracy is unclear.