the data science process single outlier can throw off regression estimate distance regression line influenced by outlier xx xx xxx xx xx xx xx xx xxx xxx xx xx xxxx xx xxx xx xxx xx xxx normal regression line row number figure the encircled point influences the model heavily and is worth investigating because it can point to region where you don' have enough data or might indicate an error in the databut it also can be valid data point data entry errors data collection and data entry are error-prone processes they often require human interventionand because humans are only humanthey make typos or lose their concentration for second and introduce an error into the chain but data collected by machines or computers isn' free from errors either errors can arise from human sloppinesswhereas others are due to machine or hardware failure examples of errors originating from machines are transmission errors or bugs in the extracttransformand load phase (etlfor small data sets you can check every value by hand detecting data errors when the variables you study don' have many classes can be done by tabulating the data with counts when you have variable that can take only two values"goodand "bad"you can create frequency table and see if those are truly the only two values present in table the values "godoand "badepoint out something went wrong in at least cases table detecting outliers on simple variables with frequency table value count good bad godo bade