Hazards of incorrect data collection 

Imagine the data collected by a bank was incorrect! It could lead to inaccurate dividend statements, incorrect addresses could mean checks sent out to wrong recipients. People could make false claims to credit cards. Government funds that are needed to be distributed would go to the wrong households. Imagine if simple data from the census or a government population study was not cleaned and wrong. It would mean wrong statistics of all kinds. If it were a welfare program study it would mean wrong people get more aid than the deserving folk; all because the data was ‘dirty’ and not cleaned.

Imagine if data from a clinical trial of a drug got mixed up. The outcome would lead to a life and death situation where a drug may have false data about it being passed or a life saver drug being rejected just because the data was not cleaned. This can happen to thesis data collected in the students’ project the data that is not cleaned could be misleading changing the entire inference and analysis of the study used in a project/thesis. In all these cases a simple cleaning process will give accesses to reliable data to remove all the glitches from it and lead to more reliable results in the statistical analysis that may follow.