![]() ![]() When Data Mining technique is explicitly used to prove a particular pre-established point of view!.When there is ‘Oversearching’ of relationships between variables.When too many hypotheses are tested without proper statistical control.When there is suboptimal model construction.When there is statistical bias, confounding or misrepresentation of the PFailure to make adjustments for statistical effects of search in large models.One way to conquer errors of “data dredging” is being stringent with “significance” levels, moving to P<0.001 or beyond. This is a typical case of “data dredging” with false positive findings, a result of looking at too many possible associations. Whereas, there is no effect between the variables and confidence level is. Where the sample size is not truly representative, there is ‘confounding’ or ‘selection bias’, or there exists too many hypotheses for a given dataset, there may occur some highly correlated data that are statistically significant. This practice of “data dredging” differs from traditional Data Mining practices. Now, the low cost of data storage has caused a rethink with all kinds of data being collected first and then searched for significant patterns. Formerly, the project / questions asked would decide what data to collect, for analysis of the same. With the evolution of Big Data a fundamentally different practice of experimental design has evolved. Which sounds absurd, but may actually throw-up significant unseen relationships (what does the App user do at lunchtime when in the vicinity of Connaught Place, New Delhi?). ![]() That is, combing data for patterns without pre-established hypotheses or objectives. Or say “snooping” into an App user’s habits for finding correlations. An example would be “fishing” in very large datasets to analyse crime clusters without understanding causation. One such DATA extraction practice is analysis of large volumes of data in the quest for ANY possible relationships. If you thought DATA was only ‘mined’ and ‘extracted’ for analysis, take a look at this frequently used method of ‘data dredging’.Īs we move over from traditional eyeballing of statistical data to dig deeper into machine based techniques, the entire process of DATA extraction gets more technique based. “If you torture the data long enough, it will confess to anything.” - Ronald Coase, British economist ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |