By removing known sources of variation such as day-by-day, blocking reduces error in statistical analysis, thus providing higher power for estimating effects and greater precision for fitting response surfaces. To keep things simple, blocks are typically modeled as fixed components of variation, in other words, treated somewhat similarly to an ordinary factor. However, handling blocks as random effects will be far more advantageous in many situations.
This 30-minute webinar will lay out the basics of blocking and demonstrate fixed versus random block effects using the latest release of powerful, yet intuitive, Stat-Ease® 360 software. The presentation is geared for experimenters, keeping the technical details to a minimum.