We have seen that any measurement, when it is reported correctly, consists of three essential pieces of information: the value, the uncertainty, and the units. These indicate not only what measurement is obtained, but also the range of reasonable measurements that could be expected if the measurement was repeated. When evaluating a measurement, people may describe a measurement as "accurate" or as "precise". These two terms have different meanings in science, even though they may be used similarly in everyday speech. In the scientific sense, precise means that the measurement is reliable to several significant figures, or equivalently that the uncertainty is small. If a precise measurement of something is made, another repeated measurement should give a result very close to the same value. However, just because repeated measurements agree with each other, that doesn't mean they are correct! Many measurands can be compared to a known or accepted value. Accuracy refers to how far a measurement is away from the accepted value. 1. Is it possible for a measurement to be precise, but not accurate? Is it possible for a measurement to be accurate, but not precise? 2. Calculate the averages and the standard deviations of the two data sets in Fig. 1. The example data sets are measurements of the acceleration due to gravity. 3. Which of the data sets is more precise? Which of the data sets is more accurate? You can see that there is a potential problem if a measurement is precise, but not accurate. For example, if a measurement of the acceleration due to gravity was reported as 9.40.1 m/s2, this measurement does not agree with the accepted value! The uncertainty in this measurement was probably underestimated. 4. Which of the data sets in Fig. 1 do you think is a better measurement of the acceleration due to gravity? The goal of experiments is generally to make measurements that are both accurate and precise.