Read an experiment honestly, judge a classifier fairly and show the result clearly: p-values, an A/B test worked end to end, classification metrics, chart choice and dashboards.
Outcomes
State what a null hypothesis and a p-value are
Read a confusion matrix
Explain why a 5% threshold produces false positives by design
Calculate and explain precision, recall, F1 and accuracy
Avoid the four most common misreadings
Recognise why accuracy misleads on imbalanced data
Covered
Learning plan
Each lesson is short enough for one sitting and ends by pointing to the next.
ExplainerHypothesis TestingDoneStarted
What a p-value is, what it is not, why five percent of tests with no real effect still come out 'significant', and what to report instead of a verdict.
TutorialExperiment DesignDoneStarted
Compare two conversion rates the careful way: the difference, a p-value, a confidence interval, and how many visitors you needed in the first place.
ExplainerSupervised LearningDoneStarted
Why a model that is 62% accurate by never predicting anything is useless, and how the confusion matrix, precision, recall and F1 tell you what a classifier really does.
ReferenceCharts & GraphsDoneStarted
Match the question you are asking to a chart type, with what each is good for and the mistake that most often spoils it.
Best practicesDashboardsDoneStarted
Dashboards fail by showing everything to everyone. Ten checks on purpose, layout, labels and trust that make one people actually use.
Questions
Open the first lesson now: nothing to install, nothing to sign up for.