There is a reason scientists have to be among the top educated academics in the world. Complicated scientific literature and data analysis is a complex activity that requires understanding and application of difficult terminology, various subject matter, and specialized comprehension of information on a level well above that of the average non-specialist.

However, if the results garnered from an experiment were only to stop at the level of the researcher, the rest of the population wouldn’t be able to benefit from the knowledge. It’s up to the scholar to turn their work into an academic paper that is able to be understood by the average reader. This is done through careful word choice to put together the content that explains the experiment from beginning to end, but when it comes to demonstrating the impact of data in particular, it helps to add a visual component. Researchers frequently use graphs like box plots in order to lend greater understanding to their work before releasing it to their audience.

Data, Observation, and Variables in Literature

Scientific experiments require layers of work to be uncovered, and then those layers are built into the research paper when the actual experimenting has been completed. Three main categories of this process include data, observation, and variables, and each of these must be explained thoroughly through textual and visual content inside the document:

Data includes the information collected while the researcher is doing the field work. Everything that is gathered through questioning and observation, or through imaging and testing, is included in the data component. It is all then used to do statistical analysis of the information collected, and then it can be added to the research report. Much of this is complicated and wouldn’t be understood by a non-specialist, so visuals are helpful for better understanding.

The next part of explaining your research is to include your observations. Because these are based on things you have measured yourself, this is another area that typically includes numbers, and graphs, particularly box plots, would be helpful for much of this type of data.

Finally, variables are determined by the data you choose. These are the whole parts that are included in your graph, under the category of data. They are the characteristics and attributes that are measurable and can have different values, like age, weight, gender, etc.

Much of this data can be confusing when it’s thrown at a reader in text only, making a visual component of your work better for greater comprehension.

Why Box Plots are Popular Graphs

For the typical career path, it’s more common to see line and bar graphs, but in research, box plots are popular. Also called “box and whisker plots,” box plots are a simple way to show distribution of data. With a glance at this type of graph, it’s easily apparent how the data fits together. Outliers can be seen quickly, and any skewing of information is broken up into percentiles or quartiles by using averages.

In a box plot, there is a five-number summary of the entire dataset. Large pools of data are averaged together to show only the minimum and maximum scores, the first quartile, third quartile, and median quartiles.

Because so much research is built on piles of data, and box plots reduce that data to the key numbers, it’s easy to see why they are popular and convenient ways to explain the researcher’s findings to the audience.

Presenting Data in Tables and Graphs

Tables and graphs, when used in research, should always speak for themselves. They can accentuate the content in the text, but the reader should be able to look at the table and get the point without needing to reference the accompanying content. However, the point of a table is also to generate enough interest in the data displayed that the reader wants to continue to learn more by heading over to the text.

This is done by making sure the data is organized clearly. The right graph, whether it’s a pie, bar, double bar, line, or box plot, is the key to this. From there, the title should be concise and relevant to the information included in the visual. The reader shouldn’t have to search to find out what the numbers stand for or what the key to reading them is (thousands, milligrams, etc.).

When the visual display can be deciphered without effort, the information contained in it can be used to apply greater research understanding to the entire paper.