Module 4
Data quality and bias: finding what is wrong before you trust a number
A number from a table looks authoritative. It has digits, it was produced by a computer and nobody argued with it. That is exactly why it is dangerous when the data behind it is wrong. Legal data is more error-prone than most: entries are made by busy people for another purpose, categories change, fields are optional and the interesting cases are often the incomplete ones. A good analyst spends more time checking data than analysing it, and reports not only the answer but how far to trust it. This module teaches a vocabulary for what can be wrong with data, a set of checks that you can run with the queries from the last module, rules for cleaning without destroying the original and the common biases that make a correct calculation give a misleading answer.
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See what is included in All-Access →General education on legal data and analytics, using invented organisations, datasets and figures. It is not legal advice. Duties about client confidentiality, data protection and the use of data about employees differ between countries and employers: check the rules that apply to you.
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