Keys for Successful Analysis – Representative Sampling & Estimation of Standard Error Calculation

In any analytical technique you get out what you put in – that is, the instrument and technique measure what is given to them. 

In this webinar we’ll explore (from a theoretical aspect, the Theory of Sampling (TOS), as first formulated by Pierre Gy) the variation seen when different samples are extracted from the bulk lot. In many instances analytical techniques get superb results on tiny amounts of sample but this has repercussions from a representative sampling perspective (think fruit cake and just sampling the raisins).  

The variation caused by the inherent heterogeneity of the material represents the best variation that can be achieved and is easily calculable.  

Our examples will be based on particle size distribution analysis but the conclusions are transferable to all other metrologies including x-ray fluorescence where elemental content variation is repeated samplings is the norm.  The sampling formulae also have implications for count length in x-ray diffraction.

Who will benefit: anyone taking measurements on an instrument and wants to understand why each sample taken from a bulk lot is different. This allows achievable specifications to be set based on the heterogeneity of the material.