The simple way to estimate how frequently a particular oligonucleotide should occur by chance is to raise one quarter to the power of the number of nucleotides in the sequence. The Markov 0 model takes this into account by setting the probability of a nucleotide to the fraction that occurs in your input sequences rather than evenly to 0.25. The Markov 1 model extends this further - putting the probability of a nucleotide occuring in the context of the previous nucleotide in the sequence. For instance if the previous nucleotide was T, the program estimates the probability of the next nucleotide being a G by looking at the distribution of nucleotides that follow T in your sequence data. The Markov 2 model takes this even futher - putting the probability of a nucleotide occurring in the context of the previous two nucleotides in the sequence.
Statisticians refer to the background as the null model.
If you are using different background data try Markov 1 or Markov 2. This setting lets you select where to get the data used to generate the background model. By default Improbizer will generate the background model from the foreground data you paste in at the top. However there are times when this approach will actually filter out the pattern that you are looking for, especially if the pattern is composed of a short repeat. Alternatively you can paste in some data to use for the background model.