Data analysis & statistics
Statistics, experiments, and analysis habits that keep conclusions honest.
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Funnel plots for comparing units of very different size
Why league tables of rates put the smallest clinics and practices at both ends, and how a funnel plot with binomial control limits and an overdispersion check fixes it.
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SPC charts for operational metrics
How to build an XmR chart for a weekly metric, read the four special-cause rules, recalculate limits after a real change, and stop reacting to RAG noise.
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Synthetic health data: what it's for and where it stops
What synthetic data is good for (teaching, pipeline testing, demos, sharing structure), what it can't support, how the main approaches differ, and why synthetic isn't automatically anonymous.
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Peeking and sequential testing
Why checking an experiment's p-value every week inflates false positives, a simulation that measures by how much, and the group sequential designs that let you look early without fooling yourself.
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Simulation for what-if questions
When a simulation beats a spreadsheet, how to build one in plain Python and NumPy, and how to handle warm-up, replications, validation and the conversation with the manager who asked.
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A/B testing in operations
How to run a fair experiment on a reminder text, a booking letter or a clinic template, from the randomisation unit and the sample size to intention to treat, confidence intervals and governance sign-off.
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Queueing basics for capacity planning
Little's law, why waits explode as occupancy nears 100%, why variability matters as much as volume, and Erlang C in a few lines of Python, applied to beds and clinics.