In the above example, if there is a motif in the training set with only 4 occurrences, MEME may still find it, but it will report 5 occurrences, one of which will be erroneous. Likewise, if a motif in the training set has 9 occurrences, MEME will probably still find it, but it will report only 8 of its occurrences. MEME chooses the number of occurrences to report for each motif by optimizing a statistical heuristic function, restricting the number of occurrences to the range you give here, or using defaults described below if you leave these fields blank. In that case, each sequence must have exactly one occurrence of each motif. In that case, there can never be more occurrences of a motif than there are sequences.
MEME chooses the optimal width of each motif individually using a statistical heuristic function.
You can choose different limits for the minimum and maximum motif widths that MEME will consider. The width of each motif that MEME reports will lie within the limits you choose. You can ask MEME to shuffle the letters in each of your input sequences. Repeat this process for each motif bearing in mind that the earlier motifs are most likely to be significant. Concluding that a motif is significant is more problematic.