If a text is 1,000 words long, it is said to have 1,000 "tokens". But a lot of these words will be repeated, and there may be only say 400 different words in the text. "Types", therefore, are the different words.
The ratio between types and tokens in this example would be 40%.
Problem with TTR
But this type/token ratio (TTR) varies very widely in accordance with the length of the text -- or corpus of texts -- which is being studied. A 1,000 word article might have a TTR of 40%; a shorter one might reach 70%; 4 million words will probably give a type/token ratio of about 2%, and so on. Such type/token information is rather meaningless in most cases, though it is supplied in a WordList statistics display. The conventional TTR is informative, of course, if you're dealing with a corpus comprising lots of equal-sized text segments (e.g. the LOB and Brown corpora). But in the real world, especially if your research focus is the text as opposed to the language, you will probably be dealing with texts of different lengths and the conventional TTR will not help you much.
WordList offers a better strategy as well: the standardised type/token ratio (STTR) is computed every n words as Wordlist goes through each text file. By default, n = 1,000. In other words the ratio is calculated for the first 1,000 running words, then calculated afresh for the next 1,000, and so on to the end of your text or corpus. A running average is computed, which means that you get an average type/token ratio based on consecutive 1,000-word chunks of text. (Texts with less than 1,000 words (or whatever n is set to) will get a standardised type/token ratio of 0.)
Setting the N boundary
Adjust the n number in Minimum & Maximum Settings to any number between 100 and 20,000.