The knowledge that any individual measurement you make in a lab will lack perfect precision often leads a researcher to choose to take multiple measurements at some independent variable level. Though no one of these measurements are likely to be more precise than any other, this group of values, it is hoped, will ' cluster ' about the true value you are trying to measure. When you take multiple measurements at some independent variable level, you have the choice of showing all of the data points you recorded, or summarizing them using descriptive statistics .
Lets take, for example, the impact energy absorbed by a metal at various temperatures.
In this case, the temperature of the metal is the independent variable being manipulated by the researcher and the amount of energy absorbed is the dependent variable being recorded. Because there is not perfect precision in recording this absorbed energy, five different metal bars are tested at each temperature level. Notice the range of energy values recorded at each of the temperatures. On the other hand, at both 0 and 20 degrees, the values range quite a bit. One way to do this is to use the descriptive statistic, mean .