Dichotomising vs keeping categories in regression I'm doing an analysis to see if having children is associated with treatment outcome.
The children variable is categorical and takes values 0-7.
When I dichotomise it into 'children' and 'no children', there is no effect.

But if I keep all the levels, there is a significant association with having 3 children.

I'm not sure how to interpret it. Should I keep all the categories? Is this a real finding or a fluke? I know dichotomising is generally not a good idea but I thought it's warranted with such a variables where Yes and No are so clearcut.
For context, this is not my main research question, I'm looking at many potential risk factors and it's just one of them so I don't necessarily need to analyse it too deeply.

This is a great example of why it is so difficult to 'hypothesize after the results are known' and why we should make analysis plans before we start, even for observational data.  Every analysis we try now will just reflect something we've already seen, and we could probably find an analysis to give whatever answer we want.
IMO we can't say whether or not having children affects your outcome based on this data. Certainly your dataset is not inconsistent with 'no effect' so this is probably how you should explain it as that's how we normally think about statistical significance.  Particularly if this also comes from a set of secondary outcomes that you are exploring.  We also can't rule out a small effect.
From a programming point of view for an 'honest' quantification of your results you might try using emmeans to get the base vs all other group contrasts, and use whatever p-value and confidence interval correction is recommended for this kind of comparison.  Or just report your initial model of 'number of kids' as a numeric, if this was supported by your theory and is what you tried initially.
Whether or not it makes sense to analyse the number of children in categories like this is up to you.  Social factors might make 3 kids qualitatively different from 2, Its down to your population and your theory.
Statistical arguments aside you could also consider the Bradford Hill criteria to check if you believe a result is real or meaningful.
Finally, if you don't need have an opinion about what this data means then you don't need to overthink it.  Just show your readers your model and your results and let them decide.  A graph like this might be instructive:

