Our objective now was to understand how data characteristics (the number of individuals, n, the number of factors, N F and their type, scenario i, ii or iii) would affect the probability of wrongly concluding that there is an interaction between the two pathogens (type I error). For a given combination of these characteristics, a thousand random seroprevalence datasets were generated and we estimated the type I error associated to each approach as the proportion of random datasets for which the P-value was below 5%.