An analytical instrument, however precise, only ever measures whatever small sample is actually loaded into it — it has no way of knowing anything about the larger batch of material that sample was supposedly drawn from. If the sample collected and prepared for analysis doesn't genuinely represent that larger batch's true overall composition, the instrument will still return a precise-looking number, but that number will describe only the unrepresentative sample actually measured, not the larger batch it was meant to stand in for. Sampling and sample preparation address exactly this problem, and a failure at this stage can't be fixed by any amount of downstream instrument precision.
A representative sample has to account for real, uneven variation
Collecting a genuinely representative sample requires accounting for how a target material's composition might actually vary across the larger batch it's drawn from — a batch of material isn't necessarily uniform throughout, and a sample collected carelessly from just one location or a single moment can easily fail to capture that real underlying variation, potentially producing a result that looks precise but is systematically biased relative to the batch's true overall average composition. Proper sampling protocols typically call for collecting material from multiple different locations or points in time across the batch, and then combining those individual sub-samples together in a way specifically designed to produce a single combined sample that genuinely reflects the larger batch's real overall composition, not just whatever one convenient location or moment happened to be sampled from.
Sample preparation can introduce its own separate errors after collection
Even a genuinely well-collected, representative sample can still be compromised by errors introduced during the subsequent sample preparation stage — the physical or chemical processing a raw collected sample typically undergoes before it's actually in a form the analytical instrument can measure. Preparation steps like grinding, dissolving, diluting or extracting a specific component from the raw sample can each introduce their own distinct sources of error if not performed carefully and consistently, including accidental contamination from the preparation equipment itself, unintended loss of the specific component being measured during processing, or an uneven, non-representative final sample even when the original larger raw sample genuinely was collected representatively. This is why rigorous analytical chemistry treats sampling and sample preparation as a distinct, carefully controlled stage of an analysis in its own right, not merely a routine administrative step to move quickly past on the way to the actual measurement.
What we're still unsure about
The general principles of representative sampling and the real, documented risks of preparation-introduced error are well-established, foundational analytical chemistry, taught consistently across the field. What's more a matter of specific technical judgement than a single universal rule is exactly which sampling and preparation protocol is genuinely appropriate for a given specific material and analysis, since the right approach depends heavily on the specific material's own physical and chemical properties, how it tends to vary internally, and what specifically is being measured — a protocol well suited to one particular material or analyte doesn't necessarily transfer directly to a different one, which is why designing an appropriate sampling and preparation protocol for a genuinely new kind of analysis remains a real, specialised skill within analytical chemistry, not something a single universal formula can settle in advance for every case.
This sits inside Sampling & Sample Preparation, one of seven topics in Analytical Chemistry, one of six domains in Chemistry, one of seventeen subjects the app can quiz you on.