Blog Archives

Four Costly Myths About Worst Case Analysis

Myth #1: Worst Case Analysis (WCA) is a rigidly defined mathematical method of determining the limits of performance of a design.

There are actually a few different types of WCA, primarily:

Extreme Value Analysis (EVA)

Statistical Analysis (Monte Carlo)


WCA+ is safer than Monte Carlo and more practical than EVA. Monte Carlo can miss small but important extreme values, and EVA can result in costly overdesign. WCA+ identifies extreme values that statistical methods can miss, and then estimates the probability that the extreme value will exceed specification limits, thereby providing the designer with a practical risk-assessment metric. WCA+ also generates normalized sensitivities and optimization, which can be used for design centering. (Ref.

Myth #2: Worst Case Analysis is optional if you do a lot of testing

To maintain happy customers and minimize liability exposure, the effects of environmental and component variances on performance must be thoroughly understood. Testing alone cannot achieve this understanding, because testing — for economic reasons — is usually performed on a very small number of samples. Also, since testing typically has a short time schedule, the effects of long-term aging will not be detected.

Myth #3: Worst Case Analysis is optional if we vary worst case parameters during testing

Initial tolerances typically play a substantial role in determining worst case performance. Such tolerances, however, are not affected by heating/cooling the samples, varying the supply voltages, varying the loads, etc.

For example, a design might have a dozen functional specs and a dozen stress specs (these numbers are usually much, much higher). To expose worst case performance, some tolerances may need to be at their low values for some of the specs, but at their high or intermediate values for other specs. First, it’s not even likely that a tolerance will be at the worst case value for a single spec. Second, it’s impossible for the tolerance to simultaneously be at the different values required to expose worst case performance for all the specs. Therefore it’s not valid to expect a test sample to serve as a worst case performance predictor, regardless of the amount of temperature cycles, voltage variations, etc. that are applied to the sample.

Myth #4: Worst Case Analysis is best done by statistics experts

No, it is far better to have WCA performed — or at least supervised — by experts in the design being analyzed, using a practical tool like WCA+ that employs minimal statistical mumbo-jumbo. Analyses (particularly cook-book statistical ones), when applied by those without expertise in the design being analyzed, often yield hilariously incorrect results.

-Ed Walker