Media & Information Literacy Grade 8: Data Journalism Numbers in the News

Grade 8 Media & Information Literacy Study Guide Topic: Data Journalism: Numbers in the News

1. The Driving Question

"If a news story says ‘crime is up 20% this year,’ how do you know if that’s a real emergency or just a trick with numbers? And why do different news outlets use the same data but tell totally different stories?"

This question matters because numbers in the news shape what we fear, what we buy, and even how we vote—but the same dataset can be sliced, stretched, or spun to fit almost any headline. By the end of this guide, you’ll be able to spot when numbers are being used to inform, persuade, or mislead.

2. The Core Idea — Built, Not Listed

Imagine you’re at a school basketball game where your team loses 50–48. The next day, two news outlets report the game:

• Outlet A: "Team Loses Heartbreaker by Just 2 Points!" (They focus on the close score.)
• Outlet B: "Team Collapses in Second Half, Down 10 Points from Last Game!" (They compare it to a previous 60–48 loss.)

Same game, same numbers—but different stories. Data journalism works the same way: reporters start with raw numbers (like crime stats, test scores, or election results), then choose how to frame them. The choices they make—what to compare, what to leave out, and how to visualize the data—can change how we feel about the story, even if the numbers themselves don’t lie.

Key Vocabulary: - Data set: A collection of numbers or facts (e.g., daily temperatures for a month, test scores for every student in a district). Example: A spreadsheet tracking how many minutes each student in your class spends on homework per night. Note: In college, data sets get huge (think millions of rows), and statisticians debate how to "clean" messy data before analyzing it.

• Context: The background information that helps you understand what the numbers mean (e.g., comparing this year’s crime rate to last year’s vs. comparing it to a city with twice the population). Example: Saying "our school’s math scores dropped 5%" sounds alarming—unless you add that the state average dropped 10%, making your school better than most. Note: In advanced journalism, context includes things like historical trends, economic conditions, or even who funded the data collection.

• Visualization: A chart, graph, or map that turns numbers into a picture (e.g., a bar graph of ice cream sales by flavor, a heat map of COVID cases by county). Example: A pie chart showing what percent of your school’s budget goes to sports, arts, and academics—but the pie chart looks bigger if the designer adds a 3D effect that makes the sports slice pop out. Note: In data science, visualizations are tools for exploration (finding patterns) as well as communication (telling a story).

• Bias (in data): When the numbers themselves are skewed because of how they were collected or who was left out (e.g., a survey about teen social media use that only asks students at one school). Example: A news story about "average teacher salaries" might use data from only wealthy suburbs, making salaries seem higher than they are nationwide. Note: In college statistics, bias is a technical problem (e.g., sampling bias, response bias) with mathematical solutions—not just a vague idea about "fairness."

3. Assessment Translation

How this appears on state assessments (e.g., ELA or media literacy tests): - Multiple choice: Questions about identifying missing context, spotting misleading visuals, or choosing the best headline for a given dataset. Distractor patterns: - Answers that ignore the source of the data (e.g., "The numbers must be true because they’re from a news article"). - Answers that confuse correlation with causation (e.g., "Ice cream sales cause drowning" because both rise in summer). - Answers that pick the most dramatic visualization, not the most accurate one.

• Short constructed response: Analyze a provided data set or graph and explain how a news outlet might spin it differently. Example prompt:

  "A news outlet reports: ‘Teen vaping rates have doubled in the past year.’ The data shows that 4% of teens vaped last year, and 8% vape this year. In 2–3 sentences, explain one way this headline could be misleading and one way it could be fair."

Proficient vs. Developing Responses: | Proficient | Developing | |----------------|----------------| | "The headline is misleading because ‘doubled’ sounds huge, but the actual numbers are still small (only 4% more teens). It’s fair because 8% is technically double 4%." | "The headline is bad because vaping is bad." (Doesn’t address the numbers.) | | "They could compare it to other risks, like car accidents, to show if 8% is actually a big deal." | "They should say ‘vaping is up a little.’" (Vague; doesn’t explain why the original was misleading.) |

Model Proficient Response:

"The headline ‘doubled’ makes it sound like a crisis, but the actual increase is only 4 percentage points. A fairer way to report it would be: ‘Teen vaping rose from 4% to 8%, but remains lower than other risks like texting while driving (30%).’ This adds context so readers don’t overreact."

4. Mistake Taxonomy

Mistake 1: Ignoring the Source of the Data Prompt:

"A news article says, ‘Study finds 70% of teens feel stressed by social media.’ The article links to a survey by ‘TeenWellness Inc.’ What’s one question you should ask about this data before believing it?"

Common Wrong Response:

"I should ask if the teens were telling the truth." (This is about honesty, not the source’s reliability.)

Why It Loses Credit: - The question asks about the data’s source, not the teens’ honesty. - A good answer must name a specific concern about who did the survey (e.g., bias, sample size).

Correct Approach:

"Who funded TeenWellness Inc.? If it’s a company that sells stress-relief apps, they might have an incentive to make stress seem worse. Also, how many teens did they survey? 70% of 10 teens isn’t the same as 70% of 10,000."

Mistake 2: Confusing "Big Numbers" with "Important Numbers" Prompt:

"A news story says, ‘City spends $10 million on new bike lanes!’ Should you be impressed? Explain your answer in 2–3 sentences."

Common Wrong Response:

"Yes, because $10 million is a lot of money." (This ignores context—$10 million might be tiny or huge depending on the city’s budget.)

Why It Loses Credit: - The question asks for an explanation, not just a yes/no. - A good answer must compare the number to something else (e.g., total budget, other expenses).

Correct Approach:

"It depends on the city’s total budget. If the city spends $1 billion a year, $10 million is only 1%—not that impressive. But if the city is small and $10 million is 10% of its budget, that’s a big deal. The story should compare it to other spending, like roads or schools."

Mistake 3: Trusting Visuals Without Questioning Them Prompt:

"A bar graph shows that ‘Crime in Our City Has Skyrocketed!’ The graph’s y-axis starts at 50 instead of 0. In 1–2 sentences, explain why this might be misleading."

Common Wrong Response:

"The graph is wrong because it doesn’t start at 0." (This is true but doesn’t explain why it’s misleading.)

Why It Loses Credit: - The question asks for an explanation of the effect, not just a rule. - A good answer must describe how the visual distorts the data.

Correct Approach:

"Starting the y-axis at 50 makes small changes look huge. If crime went from 55 to 60, the bar will look twice as tall, even though it’s only a 5-point increase. It tricks readers into thinking crime is ‘skyrocketing’ when it’s actually stable."

5. Connection Layer

1. Within Media Literacy-Algorithmic Bias: Data journalism-how social media feeds are curated — Both rely on which data is selected and how it’s framed. A news outlet might choose crime stats to scare you; Instagram’s algorithm might choose posts to keep you scrolling. In both cases, the "story" depends on what’s included and what’s left out.

2. Across Subjects-Math (Statistics): Data journalism-margin of error in polls — When a news story says "Candidate A leads by 3 points," the fine print might say "margin of error: ±4 points." That means the real lead could be anywhere from -1 to +7. Journalists often ignore this, making races seem closer (or more decisive) than they are—just like how they might ignore context in crime stats.

3. Outside School-Sports Commentary: Data journalism-ESPN’s "analytics" segments — When a commentator says "This player’s PER (Player Efficiency Rating) is 25, so he’s elite," they’re doing data journalism for sports. The number sounds objective, but PER is just one way to measure performance—and it might ignore defense, leadership, or clutch plays. Now you’ll notice when they cherry-pick stats to fit a narrative.

6. The Stretch Question

"If a news outlet reports that ‘homework causes stress’ based on a survey where 80% of stressed students said they had a lot of homework, is that a fair conclusion? What’s one other way to interpret the data—and how could you test which interpretation is right?"

Pointer Toward the Answer: The survey shows correlation (stressed students also have homework), not causation (homework causes stress). Maybe stressed students take more homework-heavy classes, or maybe they’re stressed about other things (tests, college) and homework is just the last straw. To test this, you’d need an experiment: randomly assign some students more homework and others less, then measure stress. If the homework-heavy group isn’t more stressed, the original conclusion is wrong. (Spoiler: Real studies on this are messy—which is why journalists love to oversimplify!)