I have a data set that consists of 15 age values.
I want to clean the data before doing anything further. I have a few questions about data cleaning and data integrity.
What is the best treatment if the dataset contains an illegal value (like '${\rm F1}$', e.g.)? I thought taking the mean of the 14 remaining values and doing a mean substitution might be a good idea.
If there are values that cannot be right, such as $-99$ (no one can be $-99$ years old), is the best course of action a subjective treatment? Or should I use a statistical method, such as mean substitution? It seems logical to me to turn the $-99$ to a $+99$, and treat it as a data entry error.
Can $0$ be an age? The data set contains a $0$ value.
I am mostly looking to know if there is any formalised approach to dealing with these type of problems.