Quantitative information in texts | Reading | Khan Academy

Hello readers! Today we're going to talk about quantitative information in texts. But I want to start with a question: What's the best way to describe the way a horse looks as it runs? What's the most efficient way?

I guess I could just use words, right? The horse pushes off with its back legs, and then its front legs come up. As the horse propels itself forward, both its back and front hooves are off the ground at the same time as it gallops. It's like a series of jumps. Listening to myself say that, it doesn't feel very clear to me at least. It's certainly less clear than this moving image of a galloping horse.

Sometimes, the most efficient way to express information is to present it visually — to see it in order to best understand it. And so, we're going to talk today about graphs and charts, your friends and mine. This video is no longer about horses; I apologize if I gave that impression. I did give a pretty obvious title when I said "quantitative information in texts," but to those of you who I've misled, I'm sorry.

Now graphs are one of many ways we can make visual representations of data—not information you can see, news you can use. I guess a question that follows from that is: Why do it? Why make a graph in a chart? What does that have to do with writing?

Well, visualizing data can make an argument stronger. Let's say my neighborhood is having a bake sale to raise money for, I don’t know, a carnival. Some of my neighbors have peanut allergies, so I want to write to the neighborhood council to say, "Let's make sure that the baked goods don't have peanuts in them so the whole neighborhood can participate without fear of having an allergic attack."

That's supposed to be a peanut; I realize it kind of looks like a dog treat, but let's pretend that's a peanut. Now good readers know that data is important to informational text, so as a writer, I want to make sure that I'm backing up my claims with a chart.

Here's the text of the letter I'd send to my neighborhood council:

"Dear Neighborhood Council, I'm writing to ask that you officially ban peanuts and tree nuts from the baked goods on offer during our upcoming carnival bake sale. Over a fifth of residents surveyed reported some kind of food allergy. Please see attached graph. If we want to have their full participation in this fundraiser, we cannot permit foods that will send them to the hospital.

All my best,
David."

Now, let's take a look at that graph. These are made-up numbers in a made-up situation, but let's say I interviewed 50 of my neighbors and 40 of them reported no allergies, but 10 of them do. So we can have this bar graph that breaks down those allergies by type. One person is allergic to strawberries, two people are allergic to peanuts, four people are allergic to tree nuts like pecans or walnuts, and three people have multiple food allergies. Then we have this bar here that shows all the folks that don't have food allergies—that’s our—that’s the other 40.

To put it another way, here's a pie chart of that same data set showing just how many people in the neighborhood have allergies in total. It's just a different way of looking at it. This is all the same data, but we can see that the percentage of people surveyed who have allergies adds up to 20% of the total.

Something that's in the letter but not the graphs is the idea that these are very serious allergies. If the wrong person ate a peanut, they could have a medical emergency. If the council were to look at just the graph alone, they might say, "It's not that many people; peanuts are great, let's include them." But combined with the text, the stakes become more clear, and they may better understand the consequences of having nuts at the bake sale.

Something that's in the graph that isn't in the letter is the breakdown of allergies by type. Maybe the council could decide that the one person with a strawberry allergy is an acceptable risk, and that strawberry baked goods are kind of obvious looking and easy to avoid, in a way that brownies with walnuts in them aren't.

This visual information allows the neighborhood council to get my point more efficiently than just the words alone. It helps me express a sort of complicated idea that peanuts and other allergens in the bake sale might constitute too much of a risk to my neighbors, and that they shouldn't be allowed.

Now, there are other questions like: Is this a representative data set? But that's a question that can be better answered by our statistics course. For now, think about it this way: as a reader, your job is to look at everything that is on the page, not just the text. Then think about what both the data and the words do for your understanding.

Data and informational text are two great tastes that go great together. One can support the other and vice versa. You know, like chocolate and peanut butter—oh no, sorry, bad example—like chocolate and sunflower butter. You know what I mean? You can learn anything.

David out!