Sample normalization is a technique labs use to compare the results of different samples and establish a baseline or context for how to measure them.
When you take a particular blood test, the goal is to measure specific biomarkers — things like vitamin D levels, cortisol, insulin, cholesterol and many more. But the actual blood sample you give contains more than just the biomarker you want to measure — it will also include things like white blood cells, platelets, proteins, nutrients and many other components, and in amounts that vary from person to person.
To ensure consistency of test results, labs will measure both the target biomarker and another marker in the sample, called the normalization marker. Labs do this as a kind of calibration, a formula that brings those varying amounts to the same standard.
A similar example is how a doctor will consider your height before judging how healthy your weight is — 200 pounds is interpreted differently if you’re five feet tall than it is if you clock in at seven feet. In this example, you can think of weight as the biomarker measurement and height as the normalization marker.
By adjusting the measured level of the normalization marker in the new sample, the biomarker level can be consistently compared with previous samples. Normalization corrects for any variability that exists in samples before they are analyzed to ensure the accuracy of test results.