- 7/5/2017 10:04:53 AM
@T Sweeney: Rigor, sure, but one can think one is being fully rigorous while still being subconsciously guided by the very subjectivity that data science seeks to discount and eliminate.
So yes, outreach and discipline are key -- and part of that thoroughness is a commitment to some level of collaboration. Otherwise, you're just patting yourself on the back.
- 7/4/2017 10:05:08 PM
I think what we're talking about here, Joe, is rigor -- thoroughness, discipline and outreach to pull apart methods and conclusions. Not every organization has the luxury of devoting tons of time to this, but a commitment to rigor has to make some difference.
- 7/3/2017 5:50:08 AM
@T Sweeney: Dead on. Moreover, a big part of that is checking your work against that of your peers -- and, if your answers are dramatically different, putting your heads together to account for the difference.
Part of this relies on diversity in collaboration. Not just "diversity" in the classic ways we think about it as related to protected and semi-protected classes, but also diversity of opinion and philosophy.
In an earlier comment, you mentioned the polls leading up to the 2016 Presidential Election. The funny thing is that, for all the people on one side who were shocked by the results, people on the other side weren't so much surprised. Two particular polls with the best/most accurate results over time were predicting a Trump win -- and one of the only major news sites bringing attention to these polls was the Drudge Report (a news aggregator site that is famously right-leaning). Many of the relatively few people paying attention to media narratives on both sides of the political spectrum were, accordingly, prepared for either result in what was a genuinely close race.
- 6/28/2017 11:00:09 AM
I wish that all analytics users could scrutinize their own datasets (and conclusions) with as much granularity as we're able to look at unemployment figures. A more critical eye (and some grounding in statistics) can only help improve the outcomes of analytics projects.
- by Lyndon_Henry, Blogger
- 6/8/2017 3:30:33 PM
Given the data that factors in to unemployment rates, not to mention how the mix of that data has changed over the years, it's best to treat unemployment figures as a general barometer and not a laser-sharp snapshot of the job market. Bit as we know from experience, presidents and Wall St. traders will latch on to any data bits for justification and to score polling points or major $ trading action.
I agree totally. Unemployment numbers are often treated on a par with, say, data figures for the CERN accelerator, whereas in reality they're more in a category of very wobbly or blurry data associated with the social sciences. A 0.1 point drop in unemployment rate might be hailed by the current administration and its cheerleaders as a huge accomplishment. But in reality it could be just a rounding area, or a fluke resulting from more jobseekers becoming hopeless and ceasing to even look for work ...
- 6/8/2017 1:13:43 PM
All ecellent points, Lynson. Given the data that factors in to unemployment rates, not to mention how the mix of that data has changed over the years, it's best to treat unemployment figures as a general barometer and not a laser-sharp snapshot of the job market. Bit as we know from experience, presidents and Wall St. traders will latch on to any data bits for justification and to score polling points or major $ trading action.