Data should be summarized in tables or graphs in a manner that best explains the major results. The raw data should not be presented in totality, but rather processed to reveal trends or comparisons.
The appropriate statistical analyses should be conducted to summarize data and reveal any significant differences among treatments or statistical correlation. The data should meet the sampling criteria.
Presentation of the summarized data depends on what the researcher wants and needs to illustrate. A table can be a compact way of presenting exact values or data that do not fit into a simple pattern. Tables arc also useful for classifying information. Bar graphs or histograms arc best for illustrating differences between treatments, especially if the treatment types arc discrete. Line graphs arc appropriate for plotting continuous data to show trends or interactions between two or more variables. Pic charts are best for showing and comparing relative parts of a whole. Photographs or detailed line drawings arc the best way to describe an entire object. Data connccted to geographical sites can be displayed on graphical maps. Qualitative data is best explained in written descriptions.
The data analysis should go back to the original hypothesis and experimental design. First, you should keep in mind why the data were collcctcd in the first place, making sure to process the data so that you can address your question and test your hypothesis. Second, you should consider how to convince other scientists that your interpretation of the data is sound.
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