Big Data // Big Data Analytics
10:37 AM
Connect Directly
Repost This

5 Data Science Sins To Beware

Repent, ye data scientists! Avoid these five big data evils -- or pay with your immortal soul.

OK, perhaps our fire-and-brimstone headline goes a bit overboard. Then again, maybe it is time for a dose of data science atonement, particularly if you're guilty of any of the five deadly sins summarized below.

According to Michael Walker, founder and president of the nonprofit Data Science Association, a professional organization of data scientists with more than 500 members, these big-data sins are all too common. In fact, the Association's recently penned Code of Professional Conduct is designed to establish a set of ethical standards for the burgeoning data-science industry.

Not all big-data professionals are guilty of the five deadly sins, of course, which Walker summarized in a phone interview with InformationWeek. So here they are. Do any of these data-science transgressions hit home?

Sin #1: Cherry Picking

This is where a data scientist includes only data that confirms a particular position and ignores evidence of a contradictory position. "I see this all the time," Walker said.

[ For more on ethical best practices for big-data professionals, see Data Scientists Create Code Of Professional Conduct. ]

Cherry picking is all too common in university research, according to Walker, who referenced a 2005 paper, "Why Most Published Research Findings are False," by Stanford professor John Ioannidis. "What [Ioannidis] argues, in a nutshell, is that the overwhelming majority of research that he reviewed could not be replicated," said Walker.

Here's a hypothetical scenario that illustrates cherry picking in action:

"[Researchers] create a hypothesis they want to test out," Walker said. "So they run it 999 times, and it fails. There's no evidence to confirm their hypothesis. Then they tweak it, run it again, and all of a sudden they find evidence to confirm their hypothesis." But when these same researchers publish a paper proclaiming their success, they don't mention the 999 times they failed. "I think that's very unethical," Walker said.

Sin #2: Confirmation Bias

This is where researchers favor data that confirms their hypothesis.

"When you're dealing with very large data sets, you're going to find more relationships, more correlations," said Walker. And that can lead to causation confusion, especially in high causal density environments.

1 of 2
Comment  | 
Print  | 
More Insights
InformationWeek Elite 100
InformationWeek Elite 100
Our data shows these innovators using digital technology in two key areas: providing better products and cutting costs. Almost half of them expect to introduce a new IT-led product this year, and 46% are using technology to make business processes more efficient.
Register for InformationWeek Newsletters
White Papers
Current Issue
InformationWeek Elite 100 - 2014
Our InformationWeek Elite 100 issue -- our 26th ranking of technology innovators -- shines a spotlight on businesses that are succeeding because of their digital strategies. We take a close at look at the top five companies in this year's ranking and the eight winners of our Business Innovation awards, and offer 20 great ideas that you can use in your company. We also provide a ranked list of our Elite 100 innovators.
Twitter Feed
Audio Interviews
Archived Audio Interviews
GE is a leader in combining connected devices and advanced analytics in pursuit of practical goals like less downtime, lower operating costs, and higher throughput. At GIO Power & Water, CIO Jim Fowler is part of the team exploring how to apply these techniques to some of the world's essential infrastructure, from power plants to water treatment systems. Join us, and bring your questions, as we talk about what's ahead.