drawbacks to handling numeric data as text for storing and converting to number later?

[pharmacologist]

I'm compiling datasets of numeric values obtained from non-linear regression curve fitting so that I can compile like data from different sets and analyze, so ideally I would do this using numeric fields. However I frequently have situations in which the numeric value was not calculated but was constrained in the model used and I would need to be able to determine that prior to including/excluding. The data source I'm importing from stores the values as, e.g. "=100" when it was constrained versus "100" when not. I'm considering just treating everything as text and converting later when I'm compiling the data but I'm wondering if this will be problematic or make analyzing data slower. I can't imagine ever pulling more than 200 records and 10 values from each at a time so I'm thinking it wouldn't be that much of a burden but I'm new to database design and am looking for advice.

Alternatively I could create another table to track whether the values were fitted or constrained but that seems to just add an additional layer of complexity I'd rather not deal with but would do it if treating as text would be very problematic.



Thanks

[ranman256]

The import query can convert the field:
replace([field],"=","")

then you can put it in a numeric field.