How organizations deal with outliers, those data points that occur where they’re not expected, provide useful insights into the culture and data maturity of those organizations. Outliers occurring in simple frequency graphs could be blips that occur at the extreme ends of the normal curve. In e-discovery, outliers can be documents flagged by analytics software […]

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Selection bias occurs when data are selected for analysis in a way that not all objects being evaluated are equally likely to be selected. This results in samples that are not representative of entire populations. An extreme example would be predicting the presidential race by only sampling New York City or Los Angeles, or predicting all […]

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Implicit biases – those that we form and use without explicit consideration – can wreak havoc on achieving critical goals. One such type of bias is especially damaging when designing file classification systems – confirmation bias. That is the “…tendency to search for, interpret, favor, and recall information in a way that confirms one’s preexisting […]

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