The answers here are correct, but I wanted to give some statistical background on the terms.
When we think about measuring error, errors are often phrased in terms of Type I and Type II errors
- Type I errors are the "false alarm" errors or the "boy who cried wolf" errors. They occur when something is not present, but triggers detection anyway (often due to random noise sources)
- Type II errors are the "sleeping watchman" errors. These occur when the stimulus is present, but the detector doesn't detect it.
We often tune our systems to balance these two types of errors. The more sensitive they get, the fewer type II errors we get, but we pay for it by creating more type I errors by being more sensitive to noise. Likewise, we can dull sensitivity to minimize type I errors, but it increases type II errors.
With 0% and 100%, these terms fall apart. If you are looking for "all" or "nothing," there's no way to tune the detector to see none of one type without forcing yourself to deal with tons of the other type of error.
In scientific settings, more numbers are presented (such as confidence intervals) which provide a more complete picture. However, in advertisement, nobody uses those terms because they are too technical.
As such, terms like 100% are reserved for subjective situations like a "100% satisfaction guarantee," which specifically means that you can return it for any reason at all, just by claiming "you were not satisfied."