The topics are (see article for short explanations): Differences and chance cause variation. No measurement is exact. Bias is rife. Bigger is usually better for sample size. Correlation does not imply causation. Regression to the mean can mislead. Extrapolating beyond the data is risky. Beware the base-rate fallacy. Controls are important. Randomization avoids bias. Seek replication, not pseudoreplication. Scientists are human. Significance is significant. Separate no effect from non-significance. Effect size matters. Study relevance limits generalizations. Feelings influence risk perception. Dependencies change the risks. Data can be dredged or cherry picked. Extreme measurements may mislead. I am, quite frankly, interested in all of these since they deal with developing and determining the meaning of statistics. So, I wont get into all of them, but his one is interesting: Feelings influence risk perception. Broadly, risk can be thought of as the likelihood of an event occurring in some time frame, multiplied by the consequences should the event occur. Peoples risk perception is influenced disproportionately by many things, including the rarity of the event, how much control they believe they have, the adverseness of the outcomes, and whether the risk is voluntarily or not. For example, people in the United States underestimate the risks associated with having a handgun at home by 100-fold, and overestimate the risks of living close to a nuclear reactor by 10-fold. I am reading a book on statistics where the author talks about the risk analyses the large financial institutions had the most sophisticated of programs that simply were not testing the right things. Hence, bad outcomes.
Posted on: Sat, 30 Nov 2013 18:46:00 +0000
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