People think numbers are more accurate than words although the quality of data analysis matters.
Which one do you thin is more convenient in applied research? What are the considerations?
Reflections are appreciated in advance,
If the data can be obtained quantitatively, one is on much more solid ground. However, some individuals do not clearly understand what numbers, graphs, or other numerically-based communication vehicles mean. Therefore, there are limitations to the use of numbers. In addition, unless a researcher acknowledges this point, he or she may interpret, e.g., a randomly checked numerically-based questionnaire, as real when it isn"t.
Dennis
Dennis Mazur
Hi Awoke
Actually I believe its depend on the problem. In predictive problems, if someone is interested in a selection problem (for instance identify the top 10) even using quantitative data we can categorize the response into classes, and use powerful classifiers, popular nowadays like deep learning models (Neural nets using categorical likelihoods etc). However, if one may interested in quantifying some reliable quantitative value, like incidence of cancer in some continent or country, quantitative analysis using hierarchical models can give high accurate results.
Please, if the comment was useful, recommend for others,
Both are crucial. If a quantitative analysis is performed, but cannot be explained and interpreted, it is little more than an arithmetic exercise. A description is provided without support of quantitative analysis, it is little more than opinion.
as others have commented, it depends on the problem. also you need to understand the formula that is applied to analyze the data. remember the issue of garbage in garbage out. just because if in figures does not make it necessarily the most accurate.
In my opinion it is all about the way you deliver to the audience and who are your audiences. For instance, if you want to communicate with your result with policy makers you need to show them both quantitatively and quantitatively. But, if the audiences are scientific community it is better to deliver them by the way you think they may best understand your work.
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