Chi squared test assumptions not met

I want to perform a univariate analysis to predict if a higher BMI is associated with an increased incidence of complications after surgery. I divided the BMI of patients in 4 categories (<18.5; 18.5-25; 25-30; >30) and complications in 2 categories (yes; no). I tried to use the chi squared test to perform this univariate analysis, but the assumptions were not fulfilled. The expected counts were not higher than 1 in all cells and they were smaller than 5 in more than 20% of the cells. Which test can I use to perform this univariate analysis in a 2x4 table if the assumptions of the chi squared test are not met?


TS Contributor
How large is your sample size? "The expected counts were not higher than 1 in all cells" would mean n < 8?!

Moreover, why do you artificially categorize your sample, if you have decent BMI data for use? Categorizing destroys
much information and may cause misleading results.
If you really feel a need to categorize, then you can for example still do this after the analysis with the original,
uncategorized data, for descriptive purposes.

With kind regards



TS Contributor
the issue is tacked e.g. in Spatz's book (LINK) (pages 316 and following).
When saple size (i.e., table's grand total) is large, small expeted frequenciesmight not pose a problem.
Also, I think (but not totally sure) that Fisher's exact test might be carried out in your context.

What is your sample size?
Can you post your actual data?