statistics

A p-value isn’t a victory lap. It’s a yardstick against a world where your treatment does nothing. You write down that world (the null), set your alpha (your risk budget for false alarms), and then ask: “If nothing were true, how surprising is what we saw?” If p < alpha, you’ve earned evidence to move […]

1) Two Levers, One Bill  You can squeeze the margin by lowering confidence or raising n. One costs honesty. The other costs effort. Pick your bill and pay it on purpose. 2) Use t. Full Stop.  We don’t know σ. We almost never do. That’s why t exists. Feed it df = n − 1, take the

The recipe stays simple: statistic ± (critical value × standard error). Tools can spit out the numbers. Your job is the story. Name the parameter. State the level. Share both versions… “lower–upper” and “estimate ± margin.” Then attach a verb: staff, budget, delay, pull forward. Machines calculate. You commit.

Two gates before any math: random sample and normal-or-large. No random sample? Pass. Tiny sample with no reason to believe “bell curve”? Pass. When the gates open, use t, not wishful thinking… because we almost never know the population spread. Degrees of freedom = n − 1. (Just like the last diner at the buffet:

Ninety percent confidence is not a guarantee. It’s a seatbelt. Nine trips out of ten, you arrive with your conclusion still buckled to the truth. One trip, something weird happens… snow day, breach, surprise. The miss isn’t failure; it’s the known cost of not pretending you’re psychic. Plan for the nine. Prepare for the one.

Every interval is a contract: the midpoint is your best shot; the arms admit drift. Say it out loud. “Our launch window is ten weeks ± two.” “Screen time is five and a half hours ± one.” The margin isn’t garnish… it’s the lever. Want certainty? Pay with a wider margin. Want precision? Pay with

A confidence interval isn’t swagger. It’s a promise about your process. You took a real sample, ran a repeatable recipe, and earned a range. If you replayed this moment a hundred times under the same rules, your range would catch the truth most of the time. That’s the deal. Headlines love a single number. Grown-up

1) Simulate The Story  If a sampling idea feels abstract, simulate 1,000 samples and watch the means pile up. Seeing the stack is understanding the stack. 2) One Population… Many Samples  Teach your team to speak in plural: “Across many samples, we expect…” It shifts debates from certainty to reliability. 3) Standard Error On Every Slide  Whenever you

Two classes at 80% and 90% doesn’t mean 85% overall unless the classes are the same size. Percentages are summaries with hidden denominators… bring the denominators back and compute a weighted mean. Your story changes the moment you respect the weights… and so do your decisions.

The Central Limit Theorem isn’t a spell… it’s a deal. If observations are roughly independent and from the same process, the distribution of the sample mean drifts toward normal as n grows. Break the deal (copy-paste data, time-series dependence, duplicated records) and your “bell” learns to lie. Audit independence first, celebrate normality second.