start with the idea before the implementation.
the mechanisms you need to reason about.
Sampling
Sampling is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Estimation
Estimation is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Confidence intervals
Confidence intervals is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Hypothesis testing
Hypothesis testing is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
simulate sampling distributions
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
build an interval
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
interpret a p-value carefully
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
state assumptions
ask cortex to test me →separate effect size from significance
ask cortex to test me →identify selection bias
ask cortex to test me →what usually goes wrong.
p-value worship
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
multiple comparisons
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
causal claims from correlation
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short Statistics engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.