Analysis of data with missing observations involves, firstly, constructing a suitable set of assumptions about why the data is missing in the study.
Ignoring the problem of missing data can lead to loss of statistical power and can also introduce bias. Handling missing data is a complex and active research area in statistics. It can arise due to all sorts of reasons, such as faulty machinery in lab experiments, patients dropping out of clinical trials, or non-response to sensitive items in surveys. Missing data is very common in observational and experimental research.
