Now, your management asks you to extract abnormal age behavior. Please note, when loading data, we have already defined the structure of data. Now as per business rule, the Age field will be loaded only for Male/ Female and aged among 1 to 99. Let’s take a simple example, there are date of birth (DOB), gender and Age fields where Age is extracted from the DOB column. But that's Not True, sometimes, even in structured data there are Unknown Facts and that’s where Data Dredging, Snooping and Fishing techniques come into the action. ![]() Data mining techniques extract Known Facts as it’s done only on structured datasets and mostly, we know what's there in structured data like relationships between tables and relationships of data within the same table. There are two types of Fact Findings in any analysis that ultimately assists in DSS i.e., 1) known facts 2) unknown facts. On occasions, it shows more details about something than it contains. These sometimes bypass Data Mining techniques and come up with immature conclusions. In other words, Data Dredging, Snooping, p-hacking, and Fishing share the results which require more investigation. ![]() Data Dredging, Snooping and Fishing all refer to the same behavior of data analysis BUT without proper hypothesis and relationship among datasets.ĭata mining finds results based on the correlation of data in large data sets, but Data Dredging, Snooping, p-hacking, and Fishing find results based on chance methodology.
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