LAB 01Sampling
Why averages settle down
Draw repeated samples from a right-skewed synthetic population and watch the distribution of their means change with sample size.
Twelve repeatable labs
Change one assumption. Watch the conclusion move.
Each lab uses synthetic data, a visible seed, and a plain-language summary. No sign-up and no hidden score.
What "synthetic" means here
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12 items shown
LAB 02Sampling
One value, two centers
Move one synthetic observation and compare how the mean, median, and middle 80% respond.
LAB 03Uncertainty
What 95% covers
Generate many intervals from one fixed synthetic population and count how often the method captures its known mean.
LAB 04Uncertainty
When accurate alerts mislead
Change prevalence, sensitivity, and specificity, then inspect every expected true and false alert in a synthetic population.
LAB 05Relationships
When every group wins but the total loses
Adjust the mix of two synthetic groups and see how different weights can reverse an aggregate comparison.
LAB 06Relationships
What correlation leaves out
Compare positive, negative, near-zero unstructured, and near-zero curved synthetic relationships to see what a linear summary cannot preserve.
LAB 07Communication
Points are not percent
Set a baseline and a new rate, then compare percentage points, relative change, and absolute counts side by side.
LAB 08Relationships
Why extremes drift inward
Create two noisy synthetic measurements, select the most extreme first results, and compare the same selected group on retest.
LAB 09Sampling
When more answers miss the target
Repeat a probability sample and a tilted voluntary sample, then separate shrinking random variation from persistent selection bias.
LAB 10Uncertainty
Why fair sequences form streaks
Generate binary sequences, count their runs, and compare independent trials with a mechanism that alternates too often.
LAB 11Communication
The same values on two axes
Place five synthetic rates on a full and focused scale, then measure how axis limits change visible distance without changing the data.
LAB 12Uncertainty
Twenty chances for one false alarm
Generate complete families of valid null p-values and compare an unadjusted threshold with Bonferroni family-wise control.
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