In this study, we employed three missingness mechanisms - MCAR, MAR and MNAR to investigate the effects of proportion of Missing data on descriptive and analytic statistics: Mean (Ӯ), Variance (𝝈𝝈𝟐𝟐𝒚𝒚), correlation coefficient (𝝆𝝆𝒚𝒚𝒙𝒙𝟏𝟏𝒙𝒙𝟐𝟐), coefficient of variation (cv), skewness (sk) and Kurtosis (K) which are likely situation a researcher may encounter in the field when dealing with household surveys. This study reveals that sometimes, missing data introduce systematic distortion in survey estimates and bias flows from missing data when the causes of the missing data are linked to the survey statistic measured.