Presenting Data Effectively - The key to a good graphical presentation is to select the method that best fits the data. - BioPharm International

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Presenting Data Effectively
The key to a good graphical presentation is to select the method that best fits the data.


BioPharm International
Volume 21, Issue 2


Figure 5. Two examples showing different ways to compare data sets. In the bottom graph, the two distributions have been overlaid.
The histogram plots the frequency of events where categories are ordered by x-values. If the data are continuous, they can be "binned" to create the cut points for the histogram. There is no best number of bins and different bin sizes can reveal different features of the data. Sometimes experimentation with different bins sizes can highlight the salient points of the data. A histogram shows the shape of the data distribution, which is useful for checking the assumption of normality of the data. A popular method for testing normality is the Andersen-Darling method, which can be found in most statistical software. Figure 5 shows how two data sets can be compared. The top graphic shows the two separate histograms; in the bottom graph, the two distributions have been overlaid.

CONVERTING QUANTITATIVE TO QUALITATIVE DATA

Typically, continuous data are converted to qualitative or binary response. For example, the specification for pH is between 6.9 and 7.1 but we code values within the limits as pass and those outside the limits as fail. Though this makes for an easier disposition of the lot, it does not allow for extensive data analysis during an investigation. Another common problem with data analysis occurs when the measurement system is inadequate so data is binned into a few unique values. For example, we measure a weight to the closest 0.1 mg, although the measurement device can measure to the closest 0.01 mg. This is commonly done to adhere to significant figures in a specification, but can lead to poor data analysis.

SUMMARY

Data presentation should be designed to ensure the correct conclusions. Though graphical methods give an overview of the data, more rigorous statistical methods help to separate normal variability from special cause variability. The key to a good graphical presentation is to select the method that best fits the data.

Steven Walfish is the president of Statistical Outsourcing Services, Olney, MD, 301.325.3129,
.


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