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Statistical hypothesis testing

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lurkerbelow is the only developer at his company writing unit tests. Management, developers, everyone says they want to write unit tests, but nobody does. To bring developers into line, lurkerbelow has introduced pre-commit code review (Gerrit) and continuous integration (Jenkins). Not working. "How do I motivate my fellow coworkers to write unit tests?" he asks.

Practical deomonstrations help

jimmy_keen Answers (32 votes):

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This Q&A is part of a weekly series of posts highlighting common questions encountered by technophiles and answered by users at Stack Exchange, a free, community-powered network of 90+ Q&A sites.

Mag20 wants to implement automated testing at his company. Problem is, he's tried several times before, but has failed every time. "Everyone gets excited for the first month or two," he writes. "Then, several months in, people simply stop doing it." But now seems like the right time to try bringing automated testing back to the workplace—Mag20's team of 20 experienced developers are about to embark on a big new project.

How can he finally introduce automated testing at his company?

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call -151 writes "Many years ago, a human-generated intentionally nonsense paper was accepted by the (prominent) literary culture journal Social Text. In August, a randomly-generated nonsense mathematics paper was accepted by one of the many low-tier 'open-access' research mathematics journals. The software Mathgen, which generated the accepted submission, takes as inputs author names (or those can be randomly selected also) and generates nicely TeX'd and impressive-sounding sentences which are grammatically correct but mathematically disconnected nonsense. This was reviewed by a human, (quickly, for math, in 12 days) and the reviewers' comments mention superficial problems with the submission (PDF). The references are also randomly-generated and rather hilarious. For those with concerns about submitting to lower-tier journals in an effort to promote open access, this is not a good sign!"


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Thomas H. Davenport and D.J. Patil give the rundown on what a data scientist is, what to look for and how to hire them. It's an article in Harvard Business Review, so it's geared towards managers, and I felt like I was reading a horoscope at times, but there are some interesting tidbits in there.

Data scientists don’t do well on a short leash. They should have the freedom to experiment and explore possibilities. That said, they need close relationships with the rest of the business. The most important ties for them to forge are with executives in charge of products and services rather than with people overseeing business functions. As the story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes.

I still call myself a statistician. The main difference between data scientist and statistician seems to be programming skills, but if you're doing statistics without code, I'm not sure what you're doing (other than theory).

Update: This recent panel from DataGotham also discusses the data scientist hiring process. [Thanks, Drew]

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