12.8 - Example 7 - Dilly Bean Soak Times

Example 12-7 Section

Click on the question mark for each question about the experiment to help un-pack clues toward understanding the appropriate design.

Dilly Bean Soak Time

An experiment was performed to evaluate the effect of two soak times (a pre-treatment) and three different recipes on the taste rating of "dilly beans". Batches of raw beans are first placed in large crocks and allowed to soak for either a Short or Long time. Two crocks were randomly assigned the Short and two crocks received the Long soaktimes. Following that, beans were extracted from each crock and placed into 3 individual canning jars, which then received one of three different recipes assigned at random to the 3 jars from each crock. Here is the data:

crock soaktime recipe jar rating
1 long 1 1 45
1 long 2 2 50
1 long 3 3 44
2 short 1 4 33
2 short 2 5 40
2 short 3 6 40
3 long 1 7 46
3 long 2 8 49
3 long 3 9 45
4 short 1 10 32
4 short 2 11 41
4 short 3 12 41

Experimental Design

Treatment Design


How many treatments are there?


How many levels of each treatment are there?


If there is more than 1 treatment, how are they related?

Crossed: each level of treatments occur with all levels of other treatments. (Factorial)

Nested: levels of a treatment are unique to different levels of another treatment.


Are treatments fixed or random effects?


Are there continuous covariates? (ANOCOVA)

Randomization Design


What is the experimental unit?

An experimental unit is defined as that which receives a treatment, (e.g., plant, person, plot of ground, petri dish, etc.).

Is there more than one experimental unit? (Split Plot)


How are treatment levels assigned to experimental units?

Completely at random? CRD

Restriction or randomization?

one-dimension: RCBD

two-dimension: Latin Square


Are there sample units within experimental units? How many true replications are there?


Are there repeated measurements made on experimental units?


What is the experimental design?

A two factor treatment design in a split-plot completely randomized design