Data Dredging,Snooping,p-hacking, andFishing — Learn free

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Questions 1–10 of 10

1. What behaviour do Data Dredging, Snooping, p-hacking and Fishing all refer to?
Question 1A. Data analysis performed only on structured tablesB. Data analysis without a proper hypothesis and relationship among datasetsC. Data analysis approved by a governance committeeD. Data analysis that only counts rowsE. Data analysis restricted to known facts
2. How do the findings of Data Mining differ from those of Data Dredging?
Question 2A. Data Mining is done by machines; Dredging by humansB. Data Mining uses spreadsheets; Dredging uses databasesC. Data Mining only works on small datasets; Dredging on large onesD. Data Mining finds results based on correlation in large datasets; Dredging finds results based on a chance methodologyE. There is no difference in how they find results
3. What are the two types of fact findings in any analysis that assists a decision support system?
Question 3A. Known facts and unknown factsB. Internal facts and external factsC. Numeric facts and textual factsD. Historical facts and future factsE. Approved facts and rejected facts
4. Which technique is described as extracting Known Facts because it is done only on structured datasets?
Question 4A. Data FishingB. p-hackingC. Data MiningD. Data SnoopingE. Data Dredging
5. In the date-of-birth example, the business rule loads Age only for Male or Female customers aged 1 to 99. What might Data Dredging techniques surface when asked for abnormal age behaviour?
Question 5A. Only customers aged exactly 99B. Only customers whose gender is FemaleC. Customers born after the current dateD. Customers with duplicate namesE. People with no gender defined and an age of more than 500 years
6. Why is finding customers with abnormal ages described as very critical information?
Question 6A. Because it affects the marketing budgetB. Because it matters for Customer KYC (Know Your Customer)C. Because it changes the database schemaD. Because it invalidates the data warehouseE. Because it proves the model has become AI
7. How are Data Dredging, Snooping, p-hacking and Fishing generally characterised?
Question 7A. As the gold standard of analyticsB. As a replacement for Business IntelligenceC. As a form of data backupD. As the Dark Side of Data MiningE. As a method of data encryption
8. What is the author's own view of Data Dredging, Snooping, p-hacking and Fishing in I Am Datapedia!?
Question 8A. They are techniques for Known and Unknown, and the author is more interested in knowing the UnknownB. They should be banned in every organisationC. They are identical to Data MiningD. They only work on paper recordsE. They are useful only for marketing campaigns
9. Why have Data Dredging, Snooping, p-hacking and Fishing become more famous in the Big Data era?
Question 9A. Because regulators now require themB. Because structured data has disappearedC. Because of the new, huge size of semi-structured and unstructured datasetsD. Because they are built into every databaseE. Because Data Mining is no longer used
10. Which statement about the results of Data Dredging, Snooping, p-hacking and Fishing is NOT correct?
Question 10A. They require more investigationB. They sometimes bypass Data Mining techniquesC. They can come up with immature conclusionsD. They may show more detail about something than it containsE. They never require further investigation