In a block design, both control and randomization are considered" (Experimental design, 2010, Yale). In stratified randomization, certain factors are declared to be potentially critical in influencing results and are allocated between the two different groups to ensure that only the experimental variable is emphasized (Johnson 2005). For example, when studying the effects of having fast food restaurants near schools and their effects upon student BMI, it would be easy to conclude that poorer neighborhoods tend to have more McDonald's, but that other factors besides the proximity of fast food affect poorer children's higher BMI (such as fewer supermarkets and fewer places to exercise). However, by stratifying the different groups, and having each group have the same number of children from various socioeconomic groups, the results would be more relevant if both poorer and wealthier children had higher BMIs if their schools were located near fast food establishments. This would suggest that proximity can encourage students to patronize unhealthy fast food restaurants in greater numbers, or normalize fast food consumption in their minds when they are outside of the school environment.

Ideally,...

Extraneous variables are never considered: rather the goal is always to minimize the variables, other than the experimental factor, that could influence the conclusions of the study (Johnson 2005). Some study designs, such as cross-over studies, first subject one group to the experimental factor, then in another trial reverse the status of the experimental and control group. But even in these studies, which are designed to minimize the effect of outside influences in non-random ways, having a randomized demographic balance between the two populations enhances accuracy (Johnson 2005).
References

DePoy, Elizabeth & Laura N. Gitlin. (2005) Research: Understanding and applying multiple strategies. Mosby.

Experimental design. (1997-1998). Statistics. Yale University.

Retrieved March 4, 2010 at http://www.stat.yale.edu/Courses/1997-98/101/expdes.htm

Johnson, Laura Lee. (2005). Issues in randomization. Microsoft PowerPoint. NCAM.

Retrieved March 4, 2010 at www.nihtraining.com/cc/ippcr/current/.../Randomization2005.ppt