Today I’m looking at some practical suggestions for reducing sample sizes for attribute testing. A sample is chosen to represent a population. The sample size should be sufficient to represent the population parameters such as mean and standard deviation.

You’re in an early-stage hardware startup or a tinkerer in a toolshed with a product design set to shake up the market. Not sure how to turn your idea into a product? Here’s a step-by-step guide to the product development journey.

I’ll admit it: After five decades watching U.S. companies turning to simplistic accounting tricks to remain profitable, I’m discouraged.

The difference between common (or random) cause and special (or assignable) cause variation is the foundation of statistical process control (SPC).

Meetings give me a rash. A really bad one. One that not even calamine lotion can soothe. The only things worse than meetings are reports. Standard daily reports, weekly reports, hourly reports. Reports on the status of reports.

If you could experience the perfect workday, what would you be doing? Have you ever taken the time to think about it? 

As we learned last month, the precision to tolerance ratio is a trigonometric function multiplied by a scalar constant. This means that it should never be interpreted as a proportion or percentage.

Let’s start with a definition of Industry 4.0, keeping in mind that we’re rapidly approaching Industry 5.0. Industry 4.0 is an era marked by enhanced digitization and the increased connectivity of smart technologies.

An increasing number of engineers are embracing design for manufacturing (DFM) to streamline their production workflow. Industry leaders such as Apple, GE, and Samsung have already adopted DFM as part of their standard practices.

The success run theorem is one of the most common statistical rationales for sample sizes used for attribute data.