For a project I am working on I received raw data from many meteorology stationes.
Is it right practice to test this data against air temperature from noaa's ncep/ncar data?
No. One should not attempt to validate raw in situ measurements with reanalysis data. Reanalysis data contain partial information from observations, but are mostly model fields and are provided at very coarse resolution (~1$^\circ$ longitude). In situ measurements are subject to very local, small scale effects, and have different meaning from reanalysis. One is always free to compare the two datasets, but this would not be validation in any meaningful way.
Many meteorological stations are assimilated into NOAA's reanalysis. You should be careful with this approach as the raw data you have might have been already included in the hindcast.
The reanalysis includes the data in an statistical way. In general, the reanalysis does not match the data at any point, but tries to minimize the global difference between observations and model simulation. There is no guarantee that the reanalysis is going to be close to any of the assimilated data.
Official meteorological stations should have well-calibrated thermometers. In any comparison between models and measurements, the measurements would be the reference, not the models.
One way I can see to validate ground-based in-situ temperature measurements is by carrying an SI-traceable thermometer to all the sites. Unfortunately, you cannot do this retroactively. I don't know to what degree thermometers at official meteorological stations are SI-traceable.
Another way would be to look at spatio-temporal series, and search for outliers that way. This would not be a comparison between different independent methods, but would reject a particular measurement if it is too far off compared to measurements nearby in space and time. To find the right criteria to reject data is a delicate balance, and you will certainly end up either rejecting good data or accepting bad data.