From known to unknown via a linear relationship
A standard curve is a calibration plot that transforms a measured signal (absorbance, fluorescence, peak area) into the concentration of a target analyte. First, prepare a series of solutions with known concentrations of the target. Measure the signal (optical density, fluorescence intensity, chromatography peak area) for each known standard. Plot signal vs. concentration.
If the relationship is linear (common for dilute solutions), fit a line through the points. The slope and intercept define the calibration. Now, for any unknown sample, measure its signal, plug it into the equation of the line, and solve for concentration. That concentration is your answer.
Validation and limits of the method
The quality of a standard curve depends on the range of standards used and whether the relationship truly is linear over that range. Measure standards in triplicate to assess variability. The line should pass through the origin (zero concentration gives zero signal) or be corrected for background.
Limitations are that standard curves must be redone for each assay (different conditions, different reagent batches). Signals can drift or be nonlinear at high concentrations. An unknown outside the range of the standards (extrapolation) is unreliable. Despite these caveats, standard curves are the quickest, cheapest way to quantify samples in clinical labs, food analysis, environmental monitoring, and research.