There is obviously more than one way to approach this. One approach would be to try to work out how many people in the ‘relevant population’ have the same name. The relevant population could be people living in France or perhaps Europe or even the world. The population need not be defined solely in terms of Geography. We might be willing to limit the population to people of working age. The number of people called “Peter Smith” for example may not be available but it might be possible to find the number of people with first name “Peter” and the number with a last name “Smith” and approximate something based on that. I am assuming that is how this site works: https://howmany-ofme.com/
This would be imperfect. For example, parents might purposefully avoid names like “Jack Jackson” even though both elements are individually quite common. Parents may also try to avoid the names of celebrities, well known criminals, fictional characters and names that could lead to obvious jokes. There could also be higher or lower frequencies for names that rhyme and alliterative names. Also some names may be more or less common in higher socioeconomic groups that are more likely to become legislators and senior judges. First names come in and out of fashion. For example, in the UK, Alexa has gone out of fashion probably due to the Amazon’s voice assistant. Where year of birth is available these differences could be allowed for. One further observation is that some first names are associated with months of birth so in the UK women called April, May or June are presumably less likely to have been born in December than those called Anne, Mary and Jane. There is also the issue of nominative determinism:
Another complication is that some shortened versions of names are also full names in their own right for example Elizabeth shortens to Beth but not every Beth is an Elizabeth. This would obviously increase the potential for false positives.