I've come across this chart in a couple of places recently, and I think it's an interesting study on the occasional ambiguity of language, especially in scientific literature. More specifically, it details a number of words that are consistently defined differently by the scientific community and the general public.
Any time that we attempt to speak about quantitative values in qualitative terms (especially when it pertains to risk and probability), we run the risk that our message could be lost in translation. I've often noticed this definitional disparity with probability words like "can", "could", "may", and "might"--these words can have a wide degree of variety in meaning from one person to the next, a dynamic that is discussed and studied at length here.
Usually, this kind of miscommunication is unintentional and harmless, but certain devious individuals could of course use their knowledge of these gaps in order to take advantage of the general public (cough, Dr. Oz, cough). As members of the general public, we would do well to be aware of these possible discrepancies, and to focus on the data rather than the prose that describes it. But unfortunately, that's where America's innumeracy problem really shows its teeth--when it comes to math, most Americans just don't understand. Words matter, but math matters more.