Pricing experiments with small samples: what you can actually learn
A plain-English guide to running pricing tests when your traffic or customer count is small, what small samples can tell you, and what they can’t.
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A plain-English guide to running pricing tests when your traffic or customer count is small, what small samples can tell you, and what they can’t.
A practical walkthrough of broken tracking backfill: how to find when your analytics broke, what you can honestly reconstruct, and what you have to let go.
A plain English walkthrough of Bayes theorem for solo founders, with concrete examples for reading churn alerts, A/B tests, and small-sample dashboards without overreacting to noise.
A plain-English guide to the base rate fallacy: what it is, why solo operators fall for it in churn models, fraud alerts, and A/B tests, and how to correct for it without needing a stats degree.
A plain-English guide to how to size a sample for A/B tests and business experiments when you’re running the numbers yourself, no statistics degree required.
A plain-English guide to spotting outliers in your business data, telling errors from real extreme values, and deciding what to actually do about them.
Regression analysis in plain English: how the line of best fit turns one number into a prediction, what the slope and r squared mean, and where the line lies.
How to tell a genuine seasonal pattern in your metrics from a reporting artefact caused by billing cycles, timezones, or pipeline quirks, with a practical checklist for solo operators.
How to build an honest revenue forecast for a small business: the run rate baseline, forecasting your drivers, adding growth without fooling yourself, and always a range.
Simpson’s paradox in plain English: how a trend reverses when you pool your groups, why a hidden confounder causes it, and how to catch it in your own data.