Joseph Blitzstein: "The Soul of Statistics" | Harvard Thinks Big 4 | Summary and Q&A

Summary

Professor Blitznein, a professor of the practice of statistics, gives a talk titled "The Soul of Statistics." He begins by sharing a motivating example about British bombers during World War II and how statistician Abraham Wald advised them to put armor where the planes showed little or no damage. This example illustrates selection bias in statistics. Professor Blitznein emphasizes that conditioning is the soul of statistics, which means that all probabilities are conditional and our beliefs should be updated based on the information we have. He discusses the importance of understanding sampling and the danger of ignoring it, using the example of networks. Additionally, he explores selection bias and censoring in a longevity study and regression towards the mean in test scores. He concludes by discussing the conditional golden rule, which guides his teaching philosophy.

Questions & Answers

Q: What was the motivating example Professor Blitznein shared regarding British bombers during World War II?

Professor Blitznein shared the example of British bombers being shot down by the Nazis and how statistician Abraham Wald advised them on where to put armor. Instead of putting armor where the planes sustained heavy damage, Wald suggested putting armor where there was little or no damage on the planes that returned. This example illustrates the concept of selection bias in statistics.

Q: What does Professor Blitznein mean when he says "conditioning is the soul of statistics"?

By saying "conditioning is the soul of statistics," Professor Blitznein emphasizes that all probabilities are conditional on the information that we have. Conditional probability allows us to update our beliefs based on the observed information. It is a fundamental aspect of statistical thinking and analysis.

Q: How does Professor Blitznein highlight the importance of sampling in statistical studies?

Professor Blitznein discusses the danger of ignoring sampling in statistical studies, using the example of networks. He mentions that many studies on networks focus on analyzing the structure without considering how the network was sampled. This omission can lead to misleading answers and interpretations if researchers are not cautious about what they are conditioning on.

Q: What example does Professor Blitznein provide regarding selection bias and censoring?

Professor Blitznein shares a longevity study conducted in 1835. He presents average longevities for different professions, such as chocolate makers, professors, clocksmiths, locksmiths, and students. He explains the selection bias in the data set, such as the small sample size of chocolate makers and the issue of conditioning on the age of students. He also mentions censoring, which refers to the fact that we only know the lifespan of individuals after they have died.

Q: How does Professor Blitznein explain regression towards the mean?

Professor Blitznein explains regression towards the mean using the example of test scores. He describes how students who initially perform exceptionally well or poorly tend to move closer to the mean when they retake the test. He also mentions Sir Francis Galton's study on heights, which showed that tall fathers have sons who are not as tall on average, while short fathers have sons who tend to be taller. Regression towards the mean is a concept that applies to various domains and helps to maintain stability in populations.

Q: What is the conditional golden rule mentioned by Professor Blitznein?

The conditional golden rule is Professor Blitznein's teaching philosophy. It is based on the principle of "do unto others as you would have done unto you," but with the condition that it applies only to individuals who share the same interests, background, and knowledge. Professor Blitznein aims to follow this rule in his teaching and believes that conditional probability, although challenging, is worth contemplating.

Takeaways

Professor Blitznein emphasizes the importance of conditioning in statistics, highlighting its role in updating our beliefs based on observed information. He discusses the dangers of selection bias, ignoring sampling, and the misconception of regression towards the mean. Additionally, he shares his teaching philosophy, which is based on the conditional golden rule. Understanding these concepts is crucial for accurate statistical analysis and interpretation.