The outcome of the presidential election clearly indicated that the model used by FiveThirtyEight was closer to the truth than that of the Princeton Election Consortium in terms of the level of uncertainty in the predictions---but not by as much as you might think. I consider the question quantitatively: what are the odds that one or the other model is right given the state-by-state results?
Should we present images using a Tinder-like design pattern? Or image grid? One of the fun challenges of UX design is creating a solution that’s just right for the business, the product and the customer. This post walks through the UX team’s process for defining and solving a problem.
When Donald Trump won the 2016 presidential election, both sides of the political spectrum were surprised. The prediction models didn't see it coming and the Nate Silvers of the world took some heat for that (although Nate Silver himself got pretty close). After this, a lot of people would probably agree that the world doesn't need another statistical prediction model.
So, should we turn our backs on forecasting models? No, we just need to revise our expectations. George Box once reminded us that statistical models are, at best, useful *approximations* of the real world. With the recent hype around data science and "money balling" this point is often overlooked.
On this day in 1936, Alan Turing stood before the London Mathematical Society and delivered a paper entitled "On Computable Numbers, with an Application to the Entscheidungsproblem", wherein he described an abstract mathematical device that he called a "universal computing engine" and which would later become known as a Turing machine. As a Stitch Fix tribute, we’ve melded a Turing machine and a 1936 Singer sewing machine.
Update, Dec 12, 2016: There is a follow up post discussing the outcome of all of this after the election results were known.
In cycling, a wheel that doesn't wobble is considered to be "true". We're happy with the email validation solution we've found here - it's *also* true, and did not involve re-inventing the wheel.
We could not have the luxury of continuous deployment if we didn’t have the discipline of a strong testing process. Tests help us ensure that each new version of the app works well, without having to go through an extensive manual QA process. Tests help us continuously deploy with confidence.
Plaid is for fall; red on Valentine’s Day; no white after Labor Day. These are fashion adages we’ve all heard before -- that even John Oliver promotes. But how true are they? The Stitch Fix Algorithms team is in a unique position to quantitatively answer these questions for the first time. Given the season, we’ve decided to first take a look at the “No white after Labor Day” claim. How real is it?
Today, I feel pretty comfortable with my relationship to Time. That's a new thing for me to be able to say. Most of my adult life has felt like a Thunderdome style battle to the death with Time.
Data science begins not with data but with questions. And sometimes getting the data necessary to answer those questions requires some ingenuity.
