The question often arises: What is the best decision science food? It’s the Romeo and Juliet sandwich, an adaptation of the popular Brazilian desert “Romeu e Julieta”.
Enter the concept of effect size. Effect size gives one a way to think about how large an effect (e.g. a height difference) is, not just the probability of the data given the null hypothesis.
When was saw this interview featuring two JDM giants, Richard Thaler and Nobel Laureate Daniel Kahneman, we knew we had to run it.
A GLOBAL VIEW The Guardian newspaper in the UK made this rather amazing interactive infographic (click through to interact) on causes of death, conditioned on age and region, around the world. They also provide, below, a display of ranked causes of death, and how they’ve changed since 1990. How do people die? One thing that […]
ONE CRAZY NUMBER We at DSN thought it would be worth memorizing some reciprocals because we have a system for remembering numbers and because it might come in handy. So, we started writing out 1/x 1/2 = .5 1/3 = .3 1/4 = .25 1/5 = .2 1/6 = .16 1/7 = .142857 1/8 = […]
We take on a reader question of whether the stadium / home team matters for making a field goal. We pulled up the data on every field goal since 2002 (over 10,000) of them and plotted the probability of scoring as a function of the stadium in which the field goal was kicked.
Decision making is rarely taught in high school, even though improved decision skills could benefit young people facing life-shaping decisions. While decision competence has been shown to correlate with better life outcomes, few interventions designed to improve decision skills have been evaluated with rigorous quantitative measures. A randomized study showed that integrating decision making into U.S. history instruction improved students’ history knowledge and decision-making competence, compared to traditional history instruction. Thus, integrating decision training enhanced academic performance and improved an important, general life skill associated with improved life outcomes.
Last week we posted a nice theory about daylight savings time, in particular, that its dates were chosen to reduce variance in the time of sunrise. It looked plausible from the graph.
We were talking to our Microsoft Research colleague Jake Hofman who suggested “why don’t you just find the optimal dates to change the clock by one hour?” So we did. We got the times of sunrise for New York City from here, threw them into R, and optimized.
The result was surprising. The dates of daylight savings time do not come close to minimizing variance in sunrise.
How did they decide when and by how much to make the “daylight savings time” adjustment?
We’ve written before about using information grids when communicating risks to the general public. We like them. Turns out they are also called pictographs and, as we learned from an email from Brian Zikmund-Fisher, icon arrays.