Ran an experiment whereby I had a factor of 5 different levels on a plant cultivar. A wide range of responses were recorded. Due to the layout of the room, I quickly realised that blocking was required as plants in one area had different growth rates compared to plants in another. I therefore had 2 blocks, A and B.

However, again, due to the layout of the room, block A had only 1 replicate of each factorial level, while block B had 2 replicates of each factorial level. Within each block, randomisation was also applied block.

How should I go about running the statistics for this experiment? Is the fact that I have 1 replicate per factor level in Block A compared to 2 replicates in Block B going to be an issue? I'm going to do the analysis in R, but am a little stumped when it comes to blocking design ...

Would appreciate if someone with more experience than I could weigh in.


1 Answer 1


There is not a problem with different blocks sizes, just use a regression model. Blocks are often represented as random effects in a mixed model, but with only two blocks, just use fixed effects.


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