repeatability
/ rih-pee-tuh-BIL-ih-tee /
Imagine the same baker, in the same kitchen, baking the same loaf five times in one morning. If all five loaves come out almost identical, the recipe has good repeatability. It is the friendliest, most controlled test of consistency you can run: one person, one method, one setting, over a short stretch of time — does the result hold steady?
Repeatability is the precision obtained when the same sample is measured many times under conditions kept as constant as possible: the same analyst, the same instrument, the same reagents, the same laboratory, all within a short period. It is usually quantified by the standard deviation or relative standard deviation of these closely matched replicates. Because everything that could vary has been held still, repeatability is the best-case, most optimistic measure of a method's precision.
Repeatability matters as the first checkpoint of method validation — if a method cannot even reproduce itself under ideal conditions, it has no hope under real-world ones. The crucial honest caveat is that it flatters: it deliberately excludes the larger variation seen on different days, with different operators, or in different labs, which is captured by reproducibility. A method with excellent repeatability may still vary substantially in routine use, so repeatability is a floor on the scatter you'll meet, never the whole story.
One analyst injects the same sample into an HPLC ten times in a single afternoon and gets peak areas with a relative standard deviation of 0.4%. That tight figure is the method's repeatability; run a month later by a colleague, the spread might widen to 2%, which is reproducibility.
Best-case precision: one analyst, one day, one machine.
Keep the pair straight: repeatability fixes conditions (same operator, day, instrument), while reproducibility deliberately varies them (different labs, days, operators) and is almost always the larger, more realistic number.