1) The video suggests the best approach to avoid being misled by statistics is to: a) A) Always trust large-scale numbers because they average out errors. b) B) Ignore all statistics unless they come from peer-reviewed journals. c) C) Examine how data is grouped, and ask whether there might be hidden factors or grouping effects. d) D) Only use raw data and never rely on published results. 2) Suppose a researcher finds that Group A has a higher success rate than Group B overall, but when split by region, Group B outperforms in every region. This phenomenon described in the video is known as: a) A) The Gambler’s Fallacy b) B) Simpson’s Paradox c) C) The Monty Hall Problem d) D) Regression Toward the Mean 3) The video gives a real-world case relating to smoking survival rates. What key insight does this case illustrate? a) A) Smokers always live longer than non-smokers. b) B) Age of participants can act as a lurking variable that reverses the apparent trend. c) C) Smoking data is always untrustworthy. d) D) Only large samples (over 10 000 people) avoid paradoxes. 4) According to the video, when is it not necessarily safe to trust overall aggregated statistics? a) A) When the data is collected by a reputable organisation. b) B) When the categories within the data differ significantly in size or condition. c) C) Only when the data is from a medical context. d) D) If the numbers look “too round” (like 50%, 60%). 5) Which term does the video introduce to describe a hidden factor that skews aggregated data? a) A) Lurking variable b) B) Sampling bias c) C) Null hypothesis d) D) Outlier effect 6) In the hospital-example in the video, one reason why Hospital B appears worse overall but actually better when broken down is because: a) A) Hospital B had fewer patients overall. b) B) Hospital A only treated patients in good health. c) C) The mix of patients’ initial health conditions was different in each hospital. d) D) Hospital B mis-recorded its survival numbers.

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