Having missing information is almost inevitable in research, but many researchers only report on complete cases. Here we review the missing data theory, missingness characteristics, look at the background information, importance of studying missing data, the most common ways of correcting for missing data then extend to Kenyan HIV/ TB co-infection setting. We review most of the existing methods of dealing with missing data and what other scholars have done in the missing data area. In the methodology section, we outline and give characteristics and features of the four methods for dealing with missing data (Analysis of complete cases only, Mean/Single imputation method, MLE method, and Multiple Imputation method.) which our study is focused on. We also test the four methods on the simulated data then apply the same procedure on the real Kenyan HIV/TB co-infection data. Results showed that analysis of data that was corrected for missingness using: complete cases only, weighted method, likelihood-based, and multiple imputation estimated the Kenyan HIV/TB co-infection rate to be 29%, 27%, 26%, and 21% respectively. The results indicate that MI is the best approach to correct for missing data as it does not overestimate the HIV & TB co-infection rate