Without a systematic way to start and keep data clean, bad data will happen.
—Donato Diorio [1]
Decisions about how to represent data measurements for your research projects have important consequences – they directly determine what kinds of statistical and visualization methods can ultimately be used for analysis and presentation of your results. This means you need to select representation types thoughtfully with your analytical goals in mind while at the same time trying to avoid any form of bias in what you decide to measure and what you anticipate your data exploration ...
