Our team have been using connected vehicle data from multiple suppliers since 2018 and it has transformed how we understand traffic and safety. It can show how fast vehicles are moving on almost every road, without the need for costly roadside sensors or repeated surveys, often with the ability to unlock data from over a decade ago. That level of coverage is a major step forward for anyone working in road safety, transport planning or network management. However, there is an important constraint sitting behind all this capability. Connected vehicle data can only ever provide a sample, not a 100% census, which has consequences for what it can and cannot reliably measure. Together with our team of statisticians and data scientists we have been investigating this. Let's consider two common scenarios. In the first, an authority wants to understand typical speeds on a residential road. The road carries relatively low traffic, perhaps only a few hundred vehicles per hour at peak. Looking at any single-hour window with a low-sample-size dataset will yield little or no usable information. There aren't enough observed vehicles to produce a reliable estimate. In the second scenario, a scheme has been introduced on a busy urban corridor, and the question is how speeds have changed from one week to the next. Here, traffic volumes are high, but the timeframe is short. Again, the sample size becomes the main problem. Even on a busy road, a small percentage of vehicles observed over a single week may not be enough to reliably detect change, especially if the changes themselves are modest. In both cases, the data exists. The question is whether it is large enough to support the conclusion being asked of it. Connected vehicle data does not observe every vehicle on the road. It captures those that