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Design of Experiments (DoE) Designer

A free tool to design smarter experiments using DoE principles.

Name
Unit
Low
High
Transform

Tests every combination of low and high levels. Best for 2–6 factors when you want full interaction information.

Replicate runs at the midpoint of all factors; used to estimate pure error.

Use if experiments will run across multiple days, batches, or operators.

Shuffle the order in which runs are executed.

Total runs: 11

About This Tool

Design of Experiments (DoE) is a systematic approach for investigating how multiple factors affect a process response. Instead of varying one factor at a time (OFAT), DoE varies several factors simultaneously in a structured way, which is more efficient, more statistically powerful, and reveals interactions between factors that OFAT can never detect.

When to Use Each Design

  • 2-Level Full Factorial: Characterization of 2–5 factors when you want every main effect and every interaction.
  • 3-Level Full Factorial: Smallest designs (2–3 factors) where you want a fully saturated quadratic model.
  • Plackett-Burman: Start here when you have many factors (up to 23) and don't know which ones matter. Highly efficient screening in 8–24 runs.
  • Box-Behnken: Response surface / optimization for 3–5 factors. Avoids extreme corner points (e.g., max pH × max conductivity), which is often desirable in bioprocessing.

Practical Tips

  • Always include center points (typically 3) to estimate pure experimental error and detect curvature.
  • Randomize run order to protect against time-based confounding (reagent degradation, column aging, operator drift).
  • Block by day when experiments span multiple days or buffer batches. This removes inter-day variability from your factor effects.
  • Set realistic factor ranges. Don't choose ranges wider than operationally feasible or you'll waste runs in regions you'd never use.