Lessons
1Name the claim before selecting the experiment35 min read
Write a hypothesis with a population, comparison, outcome, and failure condition.
- →Convert a broad idea into a falsifiable empirical claim
- →Define the estimand before computing a metric
2Pair the evidence and respect the sampling unit35 min read
Analyze a small paired result without inflating the sample size.
- →Choose a resampling unit and interpret paired evidence
3Selection changes the meaning of the winning score35 min read
Explain why a selected validation winner needs independent estimation.
- →Separate model selection from final estimation
4A benchmark result has a boundary35 min read
Write a conclusion that separates internal validity, external validity, and practical effect.
- →Limit conclusions to the tested population and conditions
- →Distinguish statistical evidence from practical value
Skills in this course
- 01Convert a broad idea into a falsifiable empirical claimConvert a broad idea into a falsifiable empirical claim.
- 02Choose a resampling unit and interpret paired evidenceChoose a resampling unit and interpret paired evidence.
- 03Separate model selection from final estimationSeparate model selection from final estimation.
- 04Limit conclusions to the tested population and conditionsLimit conclusions to the tested population and conditions.
- 05Define the estimand before computing a metricDefine the estimand before computing a metric.
- 06Distinguish statistical evidence from practical valueDistinguish statistical evidence from practical value.