Such interactions may indicate the existence of an implicit factor in the data. For example, the probability of death in patient category 1 from disease A may be higher than the probability of death in patient category 2 from disease 1 because patients in category 1 tend to be older than those in category 2. In this case, age would be an implicit factor. Outliers in the table may be due to interactions. If you are unsure whether your samples are independent, you may wish to consult a statistician or someone who is knowledgeable about the data collection scheme you are using.
Outliers are anomalous values in the data.
They may be due to recording errors, which may be correctable, or they may be due to the sample not being entirely from the same population, or they may be due to interactions between row and column classificatins. If you find outliers in your data that are not due to correctable errors, you may wish to consult a statistician as to how to proceed. If the total sample size is small, or if there are many cells in the table, then it may happen that no observations are recorded for a particular cell. These zero values in a table are sampling zeroes . However, the actual process that creates the observations may produce cells in the contingency table in which observations can never occur.