Sign in to save

Bookmark this page so you can find it later.

Sign in to save

Bookmark this page so you can find it later.

This cheat sheet compares completely randomized designs and randomized block designs in statistics experiments. Students need these designs to plan fair studies, reduce bias, and understand how treatment effects are estimated. It is especially useful when deciding whether subjects should be assigned all at once or first grouped by an important variable.

The goal is to help students recognize, describe, and choose an experimental design correctly.

In a completely randomized design, all experimental units are randomly assigned directly to treatments. In a randomized block design, units are first separated into blocks of similar units, then random assignment occurs within each block. Blocking is used when a variable is expected to affect the response but is not the main explanatory variable.

Both designs rely on random assignment so differences in response can be linked more confidently to the treatments.

Key Facts

  • In a completely randomized design, every experimental unit is assigned to one treatment using random assignment across the whole group.
  • In a randomized block design, experimental units are divided into blocks first, and treatments are randomly assigned within each block.
  • A treatment is a specific condition or combination of conditions applied to experimental units.
  • A block is a group of similar experimental units formed using a variable expected to affect the response.
  • The basic treatment effect can be compared using a difference in means, such as xˉ1xˉ2\bar{x}_1 - \bar{x}_2.
  • Blocking reduces unexplained variation by comparing treatments within similar groups instead of across the entire sample.
  • Random assignment helps balance unknown variables across treatment groups and supports cause-and-effect conclusions.
  • Use a randomized block design when a known variable, such as age, gender, skill level, or location, is likely to influence the response.

Vocabulary

Experimental Unit
An experimental unit is the individual person, animal, object, or plot of land that receives a treatment.
Treatment
A treatment is the condition or procedure applied to experimental units in an experiment.
Completely Randomized Design
A completely randomized design assigns all experimental units directly to treatment groups using random chance.
Randomized Block Design
A randomized block design groups similar experimental units into blocks before randomly assigning treatments within each block.
Block
A block is a group of experimental units that are similar in a way that may affect the response variable.
Response Variable
The response variable is the outcome measured after treatments are applied.

Common Mistakes to Avoid

  • Calling any grouped experiment a block design is wrong because blocks must be formed using a variable expected to affect the response.
  • Randomly choosing subjects but not randomly assigning treatments is wrong because random assignment is what helps justify cause-and-effect conclusions.
  • Blocking after treatments are assigned is wrong because blocks must be created before random assignment within each block.
  • Using a completely randomized design when strong natural groups exist can be inefficient because important variation may be left uncontrolled.
  • Treating blocks as treatments is wrong because treatments are the conditions being tested, while blocks are used to organize similar units.

Practice Questions

  1. 1 A teacher has 6060 students and wants to compare two review methods. Describe a completely randomized design that assigns 3030 students to each method.
  2. 2 A medical study has 8080 patients, with 4040 under age 5050 and 4040 age 5050 or older. Describe a randomized block design using age group as the blocking variable.
  3. 3 In an experiment, the mean score for Treatment A is xˉA=84\bar{x}_A = 84 and the mean score for Treatment B is xˉB=79\bar{x}_B = 79. Find the difference in means xˉAxˉB\bar{x}_A - \bar{x}_B.
  4. 4 A researcher compares two fertilizers on farms in both dry and wet regions. Explain why region might be used as a blocking variable instead of using a completely randomized design.