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<ul style="margin-top: 0in;">
	<li>
		Environmental data are inherently &lsquo;messy&rsquo;, meaning that there are many variables and they are often linked. For example, if one variable is tree species and another is amount of litterfall, they might be linked &ndash; hardwood species will tend to have more leaves falling off than softwoods. Hopefully the study design in Unit 3 has helped to minimize these &lsquo;confounding factors&rsquo; when feasible. There will be many unforeseen confounding factors or variables you wish you measured. This is typical for any type of science and should be noted for posterity in Unit 6 when students present their projects.</li>
	<li>
		Use the research question and hypothesis as guides to keep the data analysis on track. Although exploring the data can be a great learning experience, students might eventually lose focus and need redirection toward the hypothesis. A teacher might ask something like, &ldquo;What is the question you&rsquo;re asking of the data?&rdquo; to help redirect.</li>
	<li>
		Use the hypothetical graph created in Unit 4 as a template or cue to what a data product &ndash; like a graph or diagram - might look like.</li>
	<li>
		Although students may work on data analysis in separate groups, it is a good idea to compile all the data into one common spreadsheet or table so that each group has the same data to begin with.</li>
</ul>
