Data precision is one of the most important considerations when conducting scientific or statistical analysis. Commonly confused with the equally important concept of accuracy, the dart board analogy articulated by the University of Hawaii demonstrates the relationship: accurate data points average out to equal expected results, while precise data points cluster closely together, even if they aren't close to anticipated results. According to Dartmouth College, precision is a measurement of the reproducibility of a set of results. Precision in data sets is an important concept even in technology related endeavors, as shown by Kenneth E. Foote and Donald J. Huebner with the University of Texas-Austin in an analysis of Geographic Information Systems. Calculating precision is a fairly simple though somewhat subjective exercise.
Things You'll Need
- Graphical representation of a data set
- Information on the relevant units expressed in the data
- Minimum allowable margin of error in the experiment
Develop a visual representation of data points such as a scatter plot. A very simple visual representation involves plotting the corresponding dependent and independent variable values for each data point on a Cartesian coordinate system.
Assess the groupings of data points and look for patterns. Precise data manifests in clusters of data points, indicating that similar input variables correlate to similar output variables.
Apply information on the units of measurement used to collect the data to determine the average spacing between data points. A simple ruler measurement can be used to determine the distance between points on the graph, then converted using an arbitrary, convenient scale that corresponds to the units of measurements used to generate the data points. This will allow data points' precision relative to one another to be calculated by taking the average of the distances.
Compare the minimum margin of error allowed in the experiment and the average precision of the data points to determine the relative overall precision of the experiment. Different types of experiments will have greater or lesser error tolerance: an engineering project will likely require precision down to very small units, while a social experiment will likely tolerate more variance.