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Quantitative data collection is important in many fields, and principles such as eliminating errors, collecting all data, and testing multiple times should be applied. Tolerable error ranges should be determined, and outliers should not be ignored. Running multiple trials and using separate groups can minimize errors.
There are many different areas of scientific and practical interest that rely on the collection of quantitative data. The collection of quantitative data is, for example, of central importance in research-based fields such as chemistry, physics and even some branches of linguistics. It is also essential for testing and other purposes in engineering, computer science, and other data-intensive fields and projects aimed at producing a final product. The specific methods used to collect quantitative data vary dramatically between projects, but there are some data collection principles that can be widely, if not universally, applied. It is, for example, important to take all possible means to eliminate human and experimental error, collect and analyze all data rather than just those that fit one’s theories, and perform an experiment or test multiple times to check for errors.
While minimal error is occasionally acceptable, in some cases it can lead to substantial inaccuracies or even failure of a project. Whenever possible, therefore, when collecting quantitative data, one should determine the degree to which error can be tolerated. The techniques and devices used to collect quantitative data should be able to do so within this tolerable error range. If they can’t, you probably need to refine your data collection method or invent an entirely new one.
When collecting quantitative data, it is often tempting to record and use only results that match previous experiments or theoretical expectations. This is especially true when only some of the numbers collected differ significantly from the expected results. These outliers, however, can be extremely important and should not be ignored, especially if they recur in subsequent experiments. Unexpected results may indicate problems with the experimental procedure or materials, or may even suggest that existing theories on the subject of experimentation or testing are incorrect. The quantitative data collection process can only be effective and objective when the researcher collects and reports all the data.
Running multiple independent trials is an excellent way to minimize errors when collecting quantitative data. This can reveal issues such as device calibration, human error, or the effects of unforeseen and uncontrolled variables. Whenever possible, separate groups of people should run tests or experiments to gather specific quantitative data. The two groups can compare all methods and variables if they collect different results, thus allowing them to isolate particular errors that arose during the quantitative data collection process.
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