covadoe_power_analysis
Statistical power ASSUMING AN ORTHOGONAL ±1 DESIGN — prefer covadoe_design_power.
Takes only scalars, so it never sees the design and cannot check its own premise. It uses
`ncp = n_runs · effect_size²`, which requires `(X'X)⁻¹_jj = 1/n_runs` — true for two-level
factorial and Plackett-Burman designs, and FALSE for a central composite, a Box-Behnken, a
definitive screening design, or a plain factorial WITH CENTRE POINTS.
On those it is wrong by a lot, and confidently: 0.8677 against a true 0.6228 on a Box-Behnken,
0.3874 against 0.2909 on a DSD, 0.3874 against 0.2491 on a 14-run factorial with centre points
(each Monte Carlo confirmed, 2026-08-07).
Use `covadoe_design_power` for ANY design carrying centre or axial points — it builds the design
and takes the non-centrality from `(X'X)⁻¹`, needs no data, and also reports what this tool would
have claimed. Use this one only when the design really is an orthogonal two-level design.
Every response carries `assumes_orthogonal_design: true` and a `validity` note.Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| alpha | No | ||
| n_runs | Yes | ||
| api_key | No | ||
| n_terms | Yes | ||
| effect_size | Yes | ||
| residual_variance | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |