Posts tagged correlation
We’ve all heard in school that “correlation does not imply causation,” but what does imply causation?! The gold standard for establishing cause and effect is a double-blind controlled trial (or the AB test equivalent). If you’re working with a system on which you can’t perform experiments, is all hope for scientific progress lost? Can we ever understand systems that we have limited or no control over? This would be a very bleak state of affairs, and fortunately there has been progress in answering these questions in the negative! So what is causality good for? Anytime you decide to take an action, in a business context or otherwise, you’re making some assumptions about how the world operates. That is, you’re making assumptions about the causal effects of possible actions.
Often, we need fast answers with limited resources. We have to make judgements in a world full of uncertainty. We can’t measure everything. We can’t run all the experiments we’d like. You may not have the resources to model a product or the impact of a decision. How do you find a balance between finding fast answers and finding correct answers? How do you minimize uncertainty with limited resources?
A report by the Australia Institute to be released today titled “Jobs and Growth … And a Few Hard Numbers” shows that there is little correlation between economic performance and either political party. The report, which examines the economic performance of Australia under every prime minister since Menzies, also found that the “business friendliness” of a government does not appear to have much impact either.
Null Island is an imaginary island located at 0°N 0°E (hence “Null”) in the South Atlantic Ocean. This point is where the Equator meets the Prime Meridian. The concept of the island originated in 2011 when it was drawn into Natural Earth, a public domain map dataset developed by volunteer cartographers and GIS analysts. In creating a one-square meter plot of land at 0°N 0°E in the digital dataset, Null Island was intended to help analysts flag errors in a process known as “geocoding.”
“We may speculate that humans explore such invariant cues to anticipate upcoming transitions. However, individuals may interpret the cues differently; some may go deeper to identify structural-revealing characteristics to optimize and adapt their action relative to critical transitions, while others may simply ignore the signals due to biased beliefs. It’s also possible that our brain has been wired to perceive such invariance as we perform perceptual or higher-level cognitive reasoning. There’s much to investigate.” “My highly ambitious, yet scientifically unfounded, conjecture,” Moon added, “would be that the brain might be performing linear algebra-based spectral analysis to decompose the dynamics and summarize the patterns!”
Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.