Experimental Design
Experimental design is the discipline of planning studies so that observed effects can be attributed to specific causes rather than to chance or confounding factors. It determines what to vary, what to hold constant, and how to assign subjects to conditions.
itArtificial intelligence and machine learning | OpenSkills.info
Course pathWalk it in order
Look it upDip in anytime
Go furtherLeaves this page
Intro
Experimental Design
Experimental design is how you plan a study so the results mean something. Without a design, you have data but no way to tell whether an observed difference is real, caused by what you think, or an artifact of how you collected it.
Why design matters
Observational data is cheap but ambiguous. You see that customers who use feature X have higher retention, but you cannot tell whether X caused the retention or whether already-loyal customers are more likely to try X. Correlation is not causation — and the gap between them is exactly what experimental design closes.
A well-designed experiment isolates the effect of interest by controlling everything else. It tells you: this difference is real, it was caused by this treatment, and here is how confident you should be.
The core elements
Every experiment has:
Continue the course
This section is part of the paid course.
See pricing to subscribe, or log in if you already have access.
Where this skill leads
Relevant careers
See how this topic contributes to broader role-level skill maps.
Sources
- https://www.itl.nist.gov/div898/handbook/pri/pri.htm
Supports
- Factorial design structure and analysis
- Randomized block and Latin square designs
- Design selection criteria and guidelines
- Interaction effects in multi-factor experiments
- Fractional factorial designs for screening
- https://www.itl.nist.gov/div898/handbook/ppc/section1/ppc136.htm
Supports
- Distinction between correlation and causation
- Experimental design as required method for causal inference
- Factor, response, and treatment terminology
- Randomization as the mechanism for valid causal claims
- https://online.stat.psu.edu/stat503/
Supports
- Completely randomized design and analysis
- Randomized block design and blocking rationale
- Power analysis and sample size determination
- Multiple comparison corrections (Bonferroni, Tukey)
- Practical vs statistical significance distinction
- Sequential testing and peeking bias
- https://www.khanacademy.org/math/statistics-probability
Supports
- Hypothesis testing framework and p-value interpretation
- Type I and Type II errors
- Sampling distributions and central limit theorem
- Confidence interval construction
- https://seeing-theory.brown.edu/
Supports
- Visual demonstrations of probability and sampling
- Interactive hypothesis testing illustrations
- Confidence interval intuition