data for grantedlike saying that person' age is greater than years the second type of error points to inconsistencies between data sources or against your company' standardized values an example of this class of errors is putting "femalein one table and "fin another when they represent the same thingthat the person is female another example is that you use pounds in one table and dollars in another too many possible errors exist for this list to be exhaustivebut table shows an overview of the types of errors that can be detected with easy checks--the "low hanging fruit,as it were table an overview of common errors general solution try to fix the problem early in the data acquisition chain or else fix it in the program error description possible solution errors pointing to false values within one data set mistakes during data entry manual overrules redundant white space use string functions impossible values manual overrules missing values remove observation or value outliers validate andif erroneoustreat as missing value (remove or inserterrors pointing to inconsistencies between data sets deviations from code book match on keys or else use manual overrules different units of measurement recalculate different levels of aggregation bring to same level of measurement by aggregation or extrapolation sometimes you'll use more advanced methodssuch as simple modelingto find and identify data errorsdiagnostic plots can be especially insightful for examplein figure we use measure to identify data points that seem out of place we do regression to get acquainted with the data and detect the influence of individual observations on the regression line when single observation has too much influencethis can point to an error in the databut it can also be valid point at the data cleansing stagethese advanced methods arehoweverrarely applied and often regarded by certain data scientists as overkill now that we've given the overviewit' time to explain these errors in more detail