The "key words" are calculated by comparing the frequency of each word in the word-list of the text you're interested in with the frequency of the same word in the reference word-list. All words which appear in the smaller list are considered, unless they are in a stop list.
If the occurs say, 5% of the time in the small word-list and 6% of the time in the reference corpus, it will not turn out to be "key", though it may well be the most frequent word. If the text concerns the anatomy of spiders, it may well turn out that the names of the researchers, and the items spider, leg, eight, etc. may be more frequent than they would otherwise be in your reference corpus (unless your reference corpus only concerns spiders!)
To compute the "key-ness" of an item, the program therefore computes
its frequency in the small word-list
the number of running words in the small word-list
its frequency in the reference corpus
the number of running words in the reference corpus
and cross-tabulates these.
Statistical tests include:
the classic chi-square test of significance with Yates correction for a 2 X 2 table
Ted Dunning's Log Likelihood test, which gives a better estimate of keyness, especially when contrasting long texts or a whole genre against your reference corpus.
See UCREL's log likelihood site for more on these.
A word will get into the listing here if it is unusually frequent (or unusually infrequent) in comparison with what one would expect on the basis of the larger word-list.
Unusually infrequent key-words are called "negative key-words" and appear at the very end of your listing, in a different colour. Note that negative key-words will be omitted automatically from a keywords database and a plot.
Words which do not occur at all in the reference corpus are treated as if they occurred 5.0e-324 times (0.0000000 and loads more zeroes before a 5) in such a case. This number is so small as not to affect the calculation materially while not crashing the computer's processor.