Analyzing & Interpreting Data: Same or Different?

data moves & topics evidence strategies vizualizations

Data Moves & Topics January 5, 2022

Are these the same? No, but they are often used as synonyms.

Are they different? Yes, absolutely! So let’s dive into the explore each.

Analyzing Data: What is it?

We can think of analyzing data as the process of getting a sense of what is on the page, be that the data table, graph, map, etc. In other words, what visually can I see in terms of the numbers, words, or symbols that I am looking at.

This can involve organizing or reviewing the organization of data, and/or plotting the data (ideally more than one graph type), and/or calculating various summary statistics from the data or more advanced statistical analyses of the data, etc.

We can ask questions like (depending on what kind of data we are looking at):

What are the data showing me? (e.g., what do you notice? what shape does it look like?)
What are the ranges of the data? (aka not just what is the maximum and minimum, but the difference between them)
Are there any outliers? (if your students don’t know this word, no need to teach it just ask “are there any data values really different from the others?”)
How do each of the variables change? (e.g., as we have more of x there are less of y)
What is the pattern in the data? (e.g., increasing, decreasing, staying the same)

There is NO one way to analyze data, beyond the fact that you got to get in and get a sense of what is there on the page.

Image by GREedge

OK, so then what is Interpreting Data?

So glad you asked! We can think of interpreting data as the process of making meaning of what is on the page, be that what is in the data table, graph, map, etc. In other words, what do all of these things that I see (as numbers, words, or symbols) mean for the broader context of the question.

This involves making the transition from executing a task (data analysis) to critically thinking and working with the information to figure out what it means (data interpretation).

We can ask questions like (depending on what kind of data we are looking at):

What does the pattern mean? (as it relates to the variables)
How could that pattern occur? (as it relates the variables to the broader context)
What does the pattern mean to you? (i.e., “I think it means that…”)
How does the pattern relate to other things that you know or are learning? (i.e., “We are learning about [topic], so now I think that…” as it relates the variables to a part of the topic or broader context)

Just like analyzing data, there is NO one way to interpret data and NO one correct interpretation of data. In fact as long as your conclusions are supported by features in the data (aka evidence) then your interpretation is correct. Because any data we have can always be looked at from different perspectives and is always just a sample of the whole system/phenomenon we are investigating/learning about.

So, What is the issue with Conflating/combining them?

To put it the most simply, it is really hard to make sense of something until you have a sense of what it is. So if we are asking our students to make sense what it means while we are asking them to get a sense of what is there we are putting our students into cognitive overload.

At best, this leads to unnecessarily tired brains.

At worst, this leads to student misconceptions that they “can’t do it” or “aren’t good at data/numbers” and/or teacher frustrations of “they just don’t get it”.

Neither of those sound ideal. So, why are so many graphic organizers and lesson plans written around conflating or combining analyzing and interpreting data into the same thing?

Image Source: San Diego County Office of Education

Well, I am not quiet sure…but I will share three of my guesses (based on personal experience teaching, coaching other teachers, and reading learning science research literature) as to why this may be so.

Time is always limited…and we are forever being asked to do more in less time. Combining the two steps of analyzing and interpreting data together “takes” less time (at least as written out on a worksheet).
It may be the blessing and the curse of “data” finally showing up explicitly in our science standards as SEP4: Analyzing & Interpreting Data (Appendix F). From the perspective of large-scale curriculum writing and The Framework, this totally makes sense. In a broad sense analyzing (getting a sense of what is there) and interpreting (making sense of what is there) data are interconnected components of working with data. The tricky part is that on the ground, in the day-to-day of helping students these are different skill sets that they need to learn.
As experts (aka those more comfortable at looking at data) we subconsciously analyze (get a sense) and interpret (make sense) data simultaneously or in such quick succession that we are not really aware that these are two different skill sets we are employing. Magnified by the fact that very few of us were explicitly or actively taught how to work with data, let alone teach data skills, many of us may be in the tricky spot of trying to break something down we don’t even realize that we do.

I am sure there are many other reasons as to why so many current data-based lesson plans often combine these two cognitively different skill sets into one approach to making sense of data. But in doing so we are actually making things harder for our students when we do this…and harder for ourselves. If we take a beat, and separate them out from one another, it can save a lot of time, headaches, and frustrations in the long run.

Check out more here: Developing CER Capability Framework

Are you saying I have to change everything?

No. Not. At. All.

Go from what you have and revise your worksheets a bit to disentangle these two skills sets. Or adjust your slide decks. Or adapt your facilitation notes.

If you want to explore this more, check out the Developing CER Capability Framework (which explores analyzing and interpreting data as well as how that wraps up into students’ CER statements) or the other links below.

Looking for more?

Check out the February 2020. Data Literacy 101: What can we actually claim from our data? Science Scope 43(6): 20-26.
Sign up to participate in the Data Literacy Series workshops to dive into helping students develop data analysis and interpretation skills
- #2 “Create & Iterate Data Visualizations”
- #3 “Identify Patterns & Relationships in Your Data”
- #4 “Identify & Explore Variability in Your Data”
- #6 “Draw Conclusions & Make Inferences from Data”
Interested in meeting 1:1 or 1:team to discuss data analysis vs interpretation is it relates to your teaching environment, sign up for a free 30-minute video consultation time here: https://calendly.com/dataspire/coaching-session.