Keep in mind Keemei makes sure you metadata follows some basic validation steps. it does not guarantee your metadata is correct (Keemei will not be able to tell that you mislabeled something) and it does not AFAIK know check whether the values are unique. It’s also possible to have missing values.
So, there are two errors here:
The number of groups are equal to the number of samples
You have values where each sample has a unique value. This could be because the data is continous (i.e. age, sample weight) or because you legitimately have a unique categorical value for each sample. In either case, one sample does not make a distribution and statistics require a distribution, so this is a challenge.
There is only a single group or the column consisted only of missing values
Now, you have one value for all groups. This might be something like a primer pair - because its the same through out your study - a body site, or maybe a host species. You only have one group so you don’t have anything to compare and you can’t do a statistical test.
…And then, as an entirely unsolicited but related note, one sample does not a distribution make, so I would be careful of tests where you have one sample. The kruskal wallis test, in general, assumes you have at least 5 samples/group, but in microbiome data I’d recommend a lot more if you can get them.