“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” This statement, often misattributed to H. G. Wells (__1866-1946__), is a paraphrased version of the sci-fi author’s much longer passage by American mathematician Samuel Stanley Wilks (__1906-1964__). Wells’s original quote is shown at the bottom of this post.

If you saw the phrase “statistical thinking” and thought about not reading any further, it could be a symptom of the larger problem addressed in this short piece. If you care about education or the economy, I encourage you to continue reading.

Let’s begin by replacing the phrase statistical thinking with the contemporary term data literacy. Data literacy is “the ability to read, work with, analyze and argue with data” (Bhargava & D’Ignacio, __2015__). There are countless resources on data literacy available on the internet; many advocate for a data-literate workforce (see, for example, Miramount, __2019__; Ribeiro, __2020__; or Stokes, __2021__). If one of education’s primary purposes is to prepare students for work (*Phi Delta Kappan*, __2019__), then schools and districts that fail to teach data literacy are failing their students as well.

Like other American institutions, public education is slow to change with the times. In many districts, the secondary math curriculum consists of the “geometry sandwich”—*Algebra I, Geometry, Algebra II*—a course of study implemented over 60 years ago in response to the Soviet launch of __Sputnik I__. Math classes in the US tend to stress formulas and procedures, which is one reason some people have an aversion to anything mathematical (the symptom I alluded to earlier).

Meanwhile, in countries that excel at math, students are taught to integrate the branches of math and think critically and creatively to solve complex problems. These data-literate children “will use these skills throughout their lives – having them will make or break a student’s success in the modern world” (*Data Science for Everyone*, __2020__, para. 4).

I mentioned above that (a) this was a brief post, and (b) there are countless resources on data literacy. As we wrap up, here are links to a few data literacy resources:

__The____SAS Institute____offers six free video modules on data literacy__called the**Data Literacy Essentials**. (To view them, create a profile at__https://www.sas.com/profile/ui/#/create__. Select “Just Browsing” in the drop-down menu under “Affiliation With SAS.” There is no obligation.)The modules are each 25 minutes long and address the topics listed below. Modules 1-2 provide an excellent overview of data literacy. Modules 3-6 show how data literacy works by using realistic examples from the pandemic.

Why Data Literacy Matters

Data Literacy Practices

Identifying Reliable Data

Discovering the Meaning of Data

Making Data-informed Decisions

Working with Data Responsibly (i.e., data ethics)

(DS4E) “is a national movement for advancing data literacy in our K-12 education system.” The DS4E website includes links to__Data Science for Everyone____activities, units, and courses for K-12 teachers__, as well as links to__resources for students and parents__.Borrowing heavily from other sources, I created a free mini-course titled “

__Developing Children’s Data Literacy: Getting Started__.” The course provides an overview of data literacy, its importance, and ways to begin teaching data literacy to children. There are links to even more data literacy resources embedded in the course. The course can be accessed__here__.

Finally, here is the entire H. G. Wells quote:

“The great body of physical science, a great deal of the essential fact of financial science, and endless social and political problems are only accessible and only thinkable to those who have had a sound training in mathematical analysis, and the time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex world-wide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write” (__1903__, p. 189).

The story behind the rephrased quote is __here__.

*NOTE: If you would like to champion the cause of data literacy, join the *__DS4E Commitments Campaign__* and/or take the steps outlined in the *__Data Literacy Advocate Packet.__