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You won’t hear from me every day, it will be no more than 1-2 times a month. Each edition covers topics like data fluency, useful tools, case studies, and interesting information. I try and make it as practical as possible.
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HESA season isn’t where your data goes wrong. It’s where you find out.
Every year, around the same time, a particular kind of tiredness settles over data teams across the sector. HESA season. The credibility reports, the quality rules, the fields that won’t reconcile, the evening someone loses trying to work out why the figures are forty students short. If you’ve lived through it, you know the exact…
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Yes, But What Does It Actually Mean? | No. 7
Base rate neglect, the “99% accurate” test, and why a positive result often isn’t what you think Here is a question that has caught out more doctors than anyone in the medical profession would like to admit. There’s a disease that affects 1 in 1,000 people. There’s a test for it, and the test is…
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Yes, But What Does It Actually Mean? | No. 6
Correlation vs causation, the ice cream that never drowned anyone, and the difference between “moves together” and “makes it happen” Here is a genuinely true fact. On days when more ice cream is sold, more people drown. The two rise and fall together with impressive reliability. Chart ice cream sales against drownings across a year…
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Everyone can build a dashboard now. That’s the problem.
Most institutions have one: the person who became the Power BI expert by accident. They were good at Excel, which in most teams is enough to get you volunteered for anything involving data. Someone needed a dashboard, they had a go, and it worked. Then they had a go at another one, and that worked…
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Yes, But What Does It Actually Mean? | No. 5
Goodhart’s Law, the cobra bounty, and what happens when a measure becomes a target Somewhere in your house, possibly on your wrist right now, there may be a device counting your steps. Ten thousand a day is the number, and it is a perfectly reasonable stand-in for “move your body a bit more”. Right up…
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AI won’t take the bias out of your admissions. It can scale it up.
A few years ago, Amazon quietly scrapped an AI tool it had built to sift job applications. The tool had been trained on ten years of the company’s own hiring decisions, the idea being that it would learn to recognise a good candidate the way Amazon’s best recruiters did. It learned something else. It taught…
