Power is meant to be a way to manage expectations for what the analysis will be
able to provide. It is calculated by comparing the size of an important effect
to an estimate of the standard deviation that will appear on the ANOVA once the
analysis is completed. It is the probability that an important effect can be
found significant given the expected standard deviation.

Use as many of the following suggestions as possible to get the estimated power
to 80%. There is no requirement for 80% power, but we at Stat-Ease, Inc. feel
that it makes for a good design.

## Can a higher alpha risk (Type I Error rate) be tolerated?

Increase the significance threshold for power under Edit - Preferences, General
- Analysis node. Increasing the alpha raises the acceptable risk of detecting
false effects. If you are more willing to find false effects, you are more
likely to find true effects.

### If more runs are affordable…

If a full factorial’s power is less than 60%, the best method is to replicate
the whole design using Design Tools, Augment Design, Replicate Design.

For larger full-factorial designs having a power around 65%, but too many runs
to replicate in full, use the augmentation tools found under Design Tools,
Augment Design, Augment. The factorial optimal is a good choice and will allow
adding just a few runs at a time.

If the design is a fractional design, including Min-Run, Irregular, and Optimal,
the best way to increase runs is to create a new design. Click yes to Use
previous design info, and choose a larger design from the list.

### If no more runs are affordable, look at the design…

Will the changes in the factor levels produce a larger change in the response
than the stated delta? If so, use the larger estimate of delta to estimate
power. Larger effects are easier to detect.

Can the factor settings be run on a wider interval? A bigger change in the
factor settings usually translates to a bigger change in the response.

Can you be satisfied finding only larger effects? If so, increase delta.

### If increasing the size of your stated delta is not an option…

Use blocks to isolate known, yet uncontrolled sources of variation. For
instance, if the experiment will take several days, build a design with one
block per day.

### If the noise is coming from your measurement system…

Get a better measurement system – this usually means more cost.

Repeat the measurements you are making and record the average as your response.

Note

If the noise is truly coming from the process and none of the above
ways to increase power are suitable, do not run this experiment. Most likely no
significant effect will arise, and little will be learned about the process.